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Citation: Giannaros, A.; Karras, A.;
Theodorakopoulos, L.; Karras, C.;
Kranias, P.; Schizas, N.; Kalogeratos,
G.; Tsolis, D. Autonomous Vehicles:
Sophisticated Attacks, Safety Issues,
Challenges, Open Topics, Blockchain,
and Future Directions. J. Cybersecur.
Priv. 2023,3, 493–543. https://
doi.org/10.3390/jcp3030025
Academic Editor: Danda B. Rawat
Received: 29 June 2023
Revised: 24 July 2023
Accepted: 31 July 2023
Published: 5 August 2023
Copyright: © 2023 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).
Journal of
Cybersecurity
and Privacy
Review
Autonomous Vehicles: Sophisticated Attacks, Safety Issues,
Challenges, Open Topics, Blockchain, and Future Directions
Anastasios Giannaros 1, Aristeidis Karras 1,* , Leonidas Theodorakopoulos 2, Christos Karras 1,* ,
Panagiotis Kranias 3, Nikolaos Schizas 1, Gerasimos Kalogeratos 2and Dimitrios Tsolis 4
1Computer Engineering and Informatics Department, University of Patras, 26504 Patras, Greece;
giannaros@ceid.upatras.gr (A.G.); nschizas@ceid.upatras.gr (N.S.);
2Department of Management Science and Technology, University of Patras, 26334 Patras, Greece;
theodleo@upatras.gr (L.T.); gkalogeratos@upatras.gr (G.K.);
3School of Electrical and Computer Engineering, National Technical University of Athens,
15773 Athens, Greece; el16729@mail.ntua.gr (P.K.);
4Department of History and Archaeology, University of Patras, 26504 Patras, Greece; dtsolis@upatras.gr
*Correspondence: akarras@ceid.upatras.gr (A.K.); c.karras@ceid.upatras.gr (C.K.)
Abstract:
Autonomous vehicles (AVs), defined as vehicles capable of navigation and decision-
making independent of human intervention, represent a revolutionary advancement in transportation
technology. These vehicles operate by synthesizing an array of sophisticated technologies, including
sensors, cameras, GPS, radar, light imaging detection and ranging (LiDAR), and advanced computing
systems. These components work in concert to accurately perceive the vehicle’s environment,
ensuring the capacity to make optimal decisions in real-time. At the heart of AV functionality lies
the ability to facilitate intercommunication between vehicles and with critical road infrastructure—a
characteristic that, while central to their efficacy, also renders them susceptible to cyber threats. The
potential infiltration of these communication channels poses a severe threat, enabling the possibility
of personal information theft or the introduction of malicious software that could compromise
vehicle safety. This paper offers a comprehensive exploration of the current state of AV technology,
particularly examining the intersection of autonomous vehicles and emotional intelligence. We
delve into an extensive analysis of recent research on safety lapses and security vulnerabilities in
autonomous vehicles, placing specific emphasis on the different types of cyber attacks to which
they are susceptible. We further explore the various security solutions that have been proposed and
implemented to address these threats. The discussion not only provides an overview of the existing
challenges but also presents a pathway toward future research directions. This includes potential
advancements in the AV field, the continued refinement of safety measures, and the development of
more robust, resilient security mechanisms. Ultimately, this paper seeks to contribute to a deeper
understanding of the safety and security landscape of autonomous vehicles, fostering discourse on
the intricate balance between technological advancement and security in this rapidly evolving field.
Keywords:
autonomous vehicles; cyber security AV attacks; AV attacks; AV safety; emotional intelligence;
blockchain in AVs; big data and AVs; real-time decision making
1. Introduction
The advent of autonomous vehicles (AVs) represents an important progression toward
the development of intelligent transportation systems. This development prepares the
way for the emergence of brand-new opportunities to improve mobility, environmental
sustainability, and other related sectors of transportation. As a result of the development
and progression of this technology, a rising focus has been placed on fully autonomous
vehicles, also known as FAVs. FAVs represent the most advanced form of vehicular automa-
tion. In terms of the Society of Automotive Engineers (SAE) categorization, full autonomy
corresponds to level 5, which denotes complete driving automation. Vehicles at this level
J. Cybersecur. Priv. 2023,3, 493–543. https://doi.org/10.3390/jcp3030025 https://www.mdpi.com/journal/jcp
J. Cybersecur. Priv. 2023,3494
are engineered to handle all aspects of dynamic driving tasks under all conditions. They are
capable of independently operating, even when faced with challenging road and climatic
circumstances. The onus of safe operation under every driving condition is entirely on the
vehicle’s systems, requiring no human intervention. These vehicles have been developed
to be capable of handling all parts of dynamic driving activities on their own, even when
faced with difficult road and climatic circumstances. The utilization of accurate, trustwor-
thy, and dependable sensor technologies is essential to their functioning. A conceptual
representation of the functional architecture of these FAVs can be viewed in Figure 1.
Prominent examples of such vehicles in today’s landscape include the Google Driver-
less Car [
1
], AnnieWAY [
2
], and Stanford Shelley [
3
]. These vehicles leverage light imaging
detection and ranging (LiDAR) technology to detect objects and recognize traffic signs. The
sensor data collected by these technologies form a foundation in mission planning, and
the onboard automated systems use this information to make key operational decisions.
For instance, if an obstacle is detected by LiDAR, the mission plan of the vehicle adjusts to
evade a potential collision.
On the other hand, the growing complexity of AVs also brings about the emergence
of new difficulties. An important concern that is emerging is the urge to ensure the safety
and resilience of AV sensors in the face of cyber attacks. The implications of this kind
of attack have the potential to be devastating due to the fact that compromised sensor
data might lead to improper driving reactions, accidents, and even deaths [
4
]. Camera
hacking is one major concern. In addition to incorrectly reading road signs, an attacker
could also manipulate the camera feed to hide obstacles or other vehicles or create phantom
objects, leading to incorrect and potentially disastrous decision-making by the vehicle’s
AI. Similarly, LiDAR and radar systems, which are used by AVs to create a detailed 3D
map of their surroundings, could also be targeted. An attacker could potentially feed the
system false data, causing it to ’see’ obstacles that do not exist, or fail to detect those that
do. The GPS system, which is crucial for navigation, can also be a target. For instance, GPS
spoofing attacks can feed false location information to the AV, leading it to go off course or
even to dangerous locations. As an example, a hacked camera can incorrectly read a speed
limit sign, putting the lives of the vehicle’s passengers and anybody else who uses the road
in danger.
Beyond the vehicle itself, the era of interconnected autonomous vehicles is upon us,
where vehicles can communicate and share environmental data not just amongst themselves
but also with wider infrastructural systems [
5
]. Although this networking capability
enhances operational efficiency, it is susceptible to potential cyber-attacks. Embedded
control systems such as engine control units (ECUs), which currently manage functions
like electric window controls, may become vulnerable. Any malicious alteration in the
programming code of these critical components during design or implementation can
degrade hardware performance or remove crucial data, leading to potentially serious
consequences [6].
A noteworthy instance of such an intrusion was documented in [
7
], where a virus was
developed to manipulate messages transmitted via the controller area network (CAN) bus,
a vital communication system linking all vehicle components. This malware was capable of
remotely locking the doors of a vehicle by intercepting the corresponding communications
of the core system. Such security vulnerabilities related to the CAN bus pose significant
threats to driver safety and privacy, and it is crucial to undertake countermeasures [
8
]. In
this survey, and, in particular, in Table 1, we review and present the state-of-the-art surveys
in the field of autonomous vehicles and categorized them based on their scope. Moreover,
after presenting each work, we highlight the scope of our work and the gaps in the different
fields of autonomous vehicles that we aim to reduce.
J. Cybersecur. Priv. 2023,3495
Table 1. Summary of surveys of autonomous vehicles.
Reference Survey Scope
[9]
A survey of autonomous vehicles:
Enabling communication
technologies and challenges
Focuses on the development of vehicular communication technologies
and AVs surrounding data gathering using sensors.
[10]
Artificial intelligence applications in
the development of autonomous
vehicles: A survey
Provides a detailed review of the utilization of AI in supporting primary
applications in AVs, namely perception, localization & mapping, and
decision making.
[11]
Autonomous vehicles that interact
with pedestrians: A survey of
theory and practice
Explores factors influencing pedestrian behavior studies, featuring both
classical works on pedestrian–driver interaction and contemporary ones
involving autonomous vehicles.
[12]
Computer vision for autonomous
vehicles: Problems, datasets and
state of the art
Examines perception-related issues for autonomous vehicles, discussing
the modular pipeline and end-to-end learning-based approaches.
[13]Planning and decision-making for
autonomous vehicles
Offers an overview of emerging trends and challenges in the realm of
intelligent and self-driving vehicles.
[14]A review on autonomous vehicles:
Progress, methods, and challenges
Investigates the current state of research in environmental detection,
pedestrian detection, path planning, motion control, and vehicle cyber
security for autonomous vehicles.
Our Work
Autonomous Vehicles:
Sophisticated Attacks, Safety Issues,
Challenges, Open Topics,
Blockchain, and Future Directions
Our survey comprehensively investigates safety and attack vectors
associated with autonomous vehicles, identifying novel threats and
suggesting potential blockchain applications and future
research directions.
The purpose of this work is to investigate, highlight, and contribute to the understand-
ing and mitigation of the difficulties that exist in AVs by presenting all factors that frame
them, including the following:
•
We present an overview of the state of the art in this field of study by providing an
analysis that is comprehensive, specific, and up-to-date on the safety problems that are
associated with autonomous cars and the countermeasures that are related to them;
•
We analyze all potential attack vectors on autonomous vehicles, an endeavor that has
not been previously undertaken to this extent. To the best of our knowledge, this
paper represents the first comprehensive exploration of such a wide range of poten-
tial threats—a significant contribution relative to previous surveys, which typically
address only a subset of these threats;
•
We highlight and explore unresolved issues and potential research directions in this
domain, thereby creating a roadmap for future studies in academia;
•
We present a concrete survey that aims to help readers understand the broader scope
of AVs by navigating different sections in the survey and gaining knowledge that is
summarized based on all references presented here, without requiring review of all
recent works.
The remainder of this article is organized as follows. In Section 2, an overview of
autonomous vehicles (AVs) is presented. Section 3highlights security attacks on sensor
systems of AVs. In Section 4, cyber security attacks on vehicular networks are analyzed,
while in Section 5, the vulnerabilities of AVs are discussed. In Section 6, vulnerabilities in
deep neural networks and machine learning are shown, while Section 7, highlights how
big data can be applied to AVs. In Section 8, the use of blockchain in AVs is presented, with
an exploration of how it enhances security. Lastly, in Section 9, a discussion takes place,
and Section 10 concludes the article by presenting summarizing points and potential future
directions.
J. Cybersecur. Priv. 2023,3496
GPS
IMS
Cameras
Radars
3D
Scanning
Lidars
Compressed Data
Ultrasound
Sensors
Sensor
Processing
Sensor
Processing
Sensor
Processing
Sensor
Processing
Raw Data
Sensor
Gathering
Object Attributes
-) Time stamps
-) Metrics
-) Location
-) Speed
Metadata
V2V & V2I
Communications
3-D Map
Execution
Mechanism
Subsystem of Visualization
and Analytics
Driver
Status
Vehicle Actions
- Accelerate
- Deccelerate
- Rotation
No Action
Wam Action
Complement
Management
Collections Processes Actions
Figure 1. Autonomous vehicle overview.
2. Overview of Autonomous Vehicles (AVs)
The reviewed research papers delve into the various aspects of autonomous vehi-
cles (AVs), ranging from sensor technologies to user acceptance. Vargas et al. provided
insights into the roles of RADAR, LiDAR, ultrasonic cameras, and GNSS sensors in AVs
and highlighted their performance under different weather conditions [
15
]. Parekh et al.
comprehensively evaluated the technologies integral to AVs, encompassing environment
detection, pedestrian identification, path planning, motion control, and cyber security [
14
].
Tian et al. underscored the criticality of testing deep-neural-network-driven autonomous
cars to expose and rectify behaviors that could trigger potentially fatal incidents [
16
]. Lastly,
Jing et al. investigated the multitude of factors influencing public acceptance of AVs,
including perceived ease of use, attitude, social norms, trust, perceived usefulness, risk
perception, compatibility, safety, performance-to-price value, mobility, symbolic value, and
environmental friendliness [
17
]. In essence, these studies present a holistic understanding
of the complexities, technologies, and societal factors contributing to the development and
adoption of autonomous vehicles.
In terms of an overview, autonomous vehicles represent the convergence of advanced
technologies aimed at revolutionizing transportation. These vehicles leverage a spectrum
of sensors and artificial intelligence to perceive the surrounding environment and make
independent decisions. From enhancing road safety by minimizing human error to pro-
viding mobility for individuals unable to operate traditional vehicles, AVs hold significant
promise. However, their successful integration into everyday life hinges on overcoming
various challenges, including robustness against diverse weather conditions, foolproof cy-
ber security, and public acceptance. As they stand on the precipice of widespread adoption,
autonomous vehicles represent a transformative potential that could redefine the landscape
of transportation.
J. Cybersecur. Priv. 2023,3497
2.1. Essential Security Principles
Definition 1
(Data Processing)
.
Data processing, which is at the core of computer security, has
an intricate connection with information security as an entire area, which is an even broader area of
study.
Description 1
(Security Measures)
.
Antivirus software, which must carefully evaluate sensor
data in order to allow for proper responses, is an excellent example of this connection as a result of
the manner in which it works. In the process of performing their primary tasks, such systems run
the risk of accidentally turning into attractive targets for attackers who seek to take advantage of the
data-rich features they provide.
As a consequence of this, it is very necessary to apply the concepts of privacy and
security to autonomous vehicles in order to strengthen their defensive systems against
possible attacks. The basic triangle is the driving force behind these fundamental prin-
ciples. Figure 2illustrates a visual representation of the way in which the triangle of
confidentiality, integrity, and availability (CIA) applies to the data security principles of
autonomous vehicles.
Confidentiality
Integrity Availability
Encryption Authentication
Data Validation Redundant Systems
Network
Accessibility
Figure 2. Triangle of confidentiality, integrity, and availability for AVs.
•
Confidentiality : This principle underscores the importance of preventing unautho-
rized data access. Upholding confidentiality is crucial to preempt potential misap-
propriation or exploitation of sensitive information, which, if mishandled, could
compromise the safe and reliable operation of AV systems.
•
Integrity: Integrity ensures the authenticity, accuracy, and consistency of data over
their entire life cycle. This involves not only detecting unauthorized data access but
also thwarting unsanctioned data modification. Preservation of data integrity is critical
to maintaining the trustworthiness of a system and its decision-making abilities.
•
Availability: A system’s effectiveness is contingent upon the consistent availability
of its functionalities and its ability to perform as expected. In the context of AVs, any
compromise in availability could have immediate and severe safety implications.
Besides these fundamental concepts, AV security may also attempt to accomplish
additional attributes, depending on the specific circumstances and application, including
privacy, authenticity, accountability, non-repudiation, and reliability. These characteristics,
when considered together, help to provide an integrated and all-encompassing security
framework for autonomous vehicles, which helps to protect such vehicles from a wide
variety of possible threats.
J. Cybersecur. Priv. 2023,3498
2.2. Spectrum of Autonomous Vehicles
As mentioned earlier, the automation process in vehicles comprises several levels,
each denoting varying degrees of autonomy. The National Highway Traffic Safety Admin-
istration (NHTSA) [
18
] has proposed a detailed six-tier categorization to encapsulate the
spectrum of vehicle automation:
•No Automation (Level 0)
: At this level, vehicle operation is entirely under the control
of the human driver, including the core functions of steering, brakes, throttle, and
motive power.
•Driver assistance (Level 1)
: At this level, while the vehicle operation continues to be
largely controlled by the driver, certain driving assistance features, such as automatic
braking or lane assistance, may be integrated into the vehicle’s system.
•Partial Automation (Level 2)
: At this level, the vehicle is equipped with advanced
automated capabilities that can control both steering and acceleration/deceleration,
but the driver must maintain active engagement with the driving environment and be
ready to take control if required.
•Conditional Automation (Level 3)
: Vehicles at this level can handle all critical driving
functions under certain conditions. However, the driver must be ready to retake
control when the system requests, thus maintaining an active supervisory role.
•High Automation (Level 4)
: Vehicles at this level can perform all necessary driving
tasks autonomously under specific conditions. The driver has the option to control the
vehicle, but it is not a requirement, and the vehicle can operate independently when
conditions permit.
•Full Automation (Level 5)
: This level represents the epitome of vehicular automation,
where the vehicle is capable of executing all necessary driving functions throughout an
entire journey, under all driving conditions, without any form of human intervention.
2.3. Emotional Intelligence in Autonomous Vehicles
As we approach an era of fully automated vehicles, emotional intelligence has emerged
as a vital area of exploration. Emotional intelligence, defined as the capacity to comprehend,
manage, and respond appropriately to emotions in oneself and others, is a characteristic
typically attributed to humans. However, the integration of emotional intelligence into
autonomous vehicles is becoming increasingly feasible with the rapid advancements in
artificial intelligence (AI) and machine learning [19].
Autonomous vehicles, through sensor technologies, AI, and sophisticated algorithms,
already demonstrate cognitive intelligence in terms of navigating complex environments,
making split-second decisions, and communicating with other vehicles or infrastructure.
Nevertheless, to fully understand and interact with their human passengers, these vehicles
also need to possess emotional intelligence. This intersection between autonomous vehicles
and emotional intelligence could transform the passenger experience, safety, and public
acceptance of these vehicles [
20
]. Research is underway to develop systems that can identify,
comprehend, and react to the emotional states of passengers. For instance, if the vehicle
detects signs of passenger anxiety during a high-speed drive, it could respond by slowing
down or by providing reassuring communication about the journey’s safety. Furthermore,
it could tailor the in-vehicle environment, adjusting elements like lighting, temperature, or
music to help calm the passenger [21].
The integration of emotional intelligence in AVs can also enhance safety. A vehicle
that can recognize a passenger’s fatigue or medical distress could take preventative actions
such as slowing down, stopping, or even calling for medical assistance [
22
]. Moreover,
emotional intelligence in AVs could promote public acceptance. As these vehicles become
more attuned to human emotions and responsive in a human-like manner, they may help
mitigate the uncertainty or discomfort associated with relinquishing control to a machine. It
is important to note that the integration of emotional intelligence into autonomous vehicles
raises several challenges and ethical considerations, including privacy, data security, and
the risk of over-reliance on technology. Nevertheless, it represents an exciting frontier in the
J. Cybersecur. Priv. 2023,3499
evolution of autonomous vehicles, potentially contributing to a safer, more personalized,
and more human-like autonomous driving experience [23].
3. Security Attacks on AV Sensors: A Closer Examination of GPS Systems
Autonomous vehicles (AVs) substantially draw upon sensor systems for navigation
and critical decision-making. Among these, the global positioning system (GPS) holds
notable significance, providing precise geolocation, speed, and temporal data, regardless
of the vehicle’s global position and meteorological conditions. Nevertheless, as with any
technologically sophisticated system, GPS is not immune to potential security compromises.
Security compromises generally manifest in various types, classified as:
1. Spoofing Attacks
: In such instances, adversaries generate synthetic signals to mimic
legitimate GPS signals. These deceptive signals, once received by the AV, are mistaken
as authentic, thereby misleading the AV into calculating its location inaccurately.
2. Jamming Attacks
: During these attacks, the perpetrator utilizes devices that transmit
signals matching the frequency of the GPS signals, with the intent to overwhelm
the authentic signals. This interference inhibits the GPS receiver’s ability to estab-
lish a connection with the original GPS signals, effectively impeding its geolocation
determination.
3. Meaconing Attacks
: These attacks entail the interception of GPS signals, which are
then deliberately delayed before retransmission. This activity can cause the GPS
receiver to miscalculate its position.
4. Replay Attacks
: These consist of capturing GPS signals and retransmitting them
at an alternative time or location. This action can mislead the GPS receiver into
miscomputing its position or time.
Each of these attacks has severe implications for AVs, potentially leading to naviga-
tional inaccuracies or even disastrous vehicular collisions. Thus, the implementation of
robust security countermeasures to protect GPS systems against these breaches becomes an
academic and industrial priority.
3.1. The Significance and Applicability of Sensor Security in Autonomous Vehicles
Security breaches targeted at sensor systems in autonomous vehicles (AVs) have
emerged as a significant cause for concern due to their potential to compromise the vehi-
cle’s safety and the well-being of its passengers. AVs deploy a complex array of sensors,
including but not limited to cameras, LiDAR, and radar. These sensors are essential for in-
terpreting the surrounding environment and facilitating crucial decision-making processes.
The compromise of these sensor systems could result in a distorted understanding of the
environment, which could, in turn, elevate the risk of accidents and safety hazards.
Historically, there have been various demonstrations highlighting the potential of
such security attacks on AV sensors. These include sophisticated spoofing attacks capable
of deceiving sensors into acknowledging non-existent obstacles and jamming attacks that
effectively interrupt the sensors’ capabilities in accurately detecting objects. Worryingly,
the equipment needed to execute these attacks can often be simple and easy to procure,
such as a laser pointer or a radio transmitter, thus increasing the potential pool of attackers.
As AVs become more commonplace in our transportation landscape, the importance
of addressing the security vulnerabilities of AV sensors becomes increasingly clear. This
recognition underpins the need for the development and implementation of rigorous
security measures tailored to detect and neutralize attacks on AV sensors. Alongside these
security provisions, it is critical to raise public awareness regarding the potential risks
associated with security attacks on AVs.
Additionally, the creation and enforcement of comprehensive standards play a critical
role in ensuring the safety and security of AVs. These standards would serve to reinforce the
safety framework for autonomous vehicles, managing and mitigating the risks associated
with potential security breaches of their sensor systems.
J. Cybersecur. Priv. 2023,3500
In conclusion, the potential consequences of security intrusions on AV sensors are
dangerous and global, threatening the protection of both the vehicle and its occupants.
As the community moves towards a future characterized by the increasing quantity of
autonomous vehicles (AVs), it becomes vital to develop and implement robust security
measures supported by comprehensive and extensive standards in order to ensure the
ongoing safety and security of these advanced vehicles.
3.2. Vulnerability of Global Positioning Systems
In the spectrum of autonomous vehicles, global positioning systems (GPS) are integral,
facilitating accurate location tracking and route navigation. However, this reliance creates
a potential vulnerability that can be exploited by malicious entities. Given the open
accessibility of GPS technology, an adversary could potentially manipulate signals, provide
incorrect navigational instructions, or even trigger vehicle crashes, posing significant safety
threats [24].
GPS-based attacks on autonomous vehicles take two primary forms: jamming and
spoofing. Jamming involves an adversary broadcasting a more potent signal at the identical
frequency as the GPS, consequently causing temporary interference [
25
]. This type of attack
can significantly hinder the operation of autonomous vehicles, as these highly sophisticated
vehicles rely heavily on accurate GPS data for efficient navigation and overall functionality.
Spoofing, a more invisible form of attack, involves an attacker disseminating falsified
GPS signals designed to emulate authentic ones. This deceptive method leads the receiver to
inadvertently acknowledge the fraudulent signals as genuine. Typically, the process of GPS
spoofing incorporates an initial phase of GPS jamming to obstruct real signals, subsequently
followed by the broadcast of counterfeit signals, thereby fooling the system [26].
Given the intrinsically unguarded nature of public GPS systems, the singular defense
mechanism against GPS spoofing lies in the domain of authentication. Achieving this
safeguard necessitates the use of precise encryption techniques [
27
]. Regardless of these
concerns, the scientific community must address these vulnerabilities and develop effective
countermeasures to ensure the safety and dependability of autonomous vehicles in an era
of GPS-centric navigation.
Due to the design of GPS systems, which are inclined to accept stronger signals, a
well-executed attack can imperceptibly alter a vehicle’s location by transmitting a strong,
fake signal [
28
]. Various countermeasures, such as monitoring of identification codes,
satellite signals, and timing intervals, have been proposed to combat these attacks. It is
known, for instance, that the expected signal strength is approximately 163 decibel watts,
so a countermeasure could consist of blocking signals with higher frequencies [
29
]. In
their research, the authors developed an antenna-array-based hybrid antijamming and
antispoofing method for GPS receivers. Using a compressed sensing framework, they
determine the direction of arrival (DOA) of the despreading satellite navigation signal and
identify the deception signal following the elimination of the interference via subspace
projection. The receiver uses adaptive multi-beamforming to accomplish undistorted
reception of the authentic satellite signal and to suppress deception based on the DOA of
the authentic and spoofing signals.
However, these measures can be circumvented if the attacker’s signals are sophisti-
cated enough to mimic genuine ones convincingly, causing the validation checks to fail and
resulting in the manipulation of the GPS device. Currently, an entirely foolproof and prac-
tical solution to these GPS-based attacks remains inaccessible. The use of military-grade
cryptography currently stands as the only feasible solution that can definitively prevent
both GPS jamming and spoofing attacks.
In the broader perspective of emotional intelligence, the ability of autonomous vehicles
to recognize and react appropriately to these threats is a significant challenge. Just as
humans must navigate and respond to deceptive cues in social contexts, autonomous
vehicles must discern between genuine and deceptive signals in their environment. This
J. Cybersecur. Priv. 2023,3501
ability to perceive and react appropriately under a potential attack is crucial to the future
development and widespread acceptance of autonomous vehicles.
3.3. Exposure of Light Detection and Ranging (LiDAR) Systems to Security Attacks
LiDAR systems which are essential components in autonomous vehicles, function as
range-finding sensors. By emitting light pulses and measuring the time taken for these
pulses to reflect off distant surfaces, they generate a three-dimensional map of the vehicle’s
environment. These laser pulses, which are produced hundreds of times per second, are
typically reflected off a rotating mirror, creating a scan. Extra pulses, referred to as echoes,
contribute to the vehicle’s ability to detect objects under varied weather conditions [30].
Despite their critical role in facilitating safe autonomous navigation, LiDAR systems
are susceptible to potential security threats. One such threat is relay signal attacks, a
variation of replay attacks, which aim to misplace targets from their actual positions
by rebroadcasting the original signal provided by the target vehicle’s LiDAR from an
altered location. This attack can be executed inexpensively using just two transceivers, as
demonstrated in [31].
Another concerning threat is spoofing signal attacks, an extension of relay signal
attacks. An adversary can create phantom objects by transmitting a signal of the same
frequency as the scanner. LiDAR systems typically listen for incoming reflections for a
minimum of 1.33 microseconds. To successfully inject signals into LiDAR, the false signal
must enter this window, which allows an attacker to manipulate the perceived location of
objects by delaying the initial signal before relaying it.
Such attacks can cause autonomous vehicles to slow down or even stop, potentially
resulting in fatal accidents, particularly on highways [
32
–
34
]. Several potential counter-
measures can be employed, including the use of non-predictable LiDAR, which skips a
pulse but continues to listen for incoming pulses. Another strategy is to reduce the LiDAR
pulse time, thereby reducing the attack window in the sensor, although this also shortens
the sensor’s operational range [
31
]. Given that LiDAR pulses are not currently encoded,
one promising direction for future research is the exploration of LiDAR pulse encoding
as a potential means of mitigating these attacks. Figure 3is a visual representation of the
Relaying and Spoofing signal attacks on LiDAR.
Delay
Component
Attack
Laser
Photodiode
Lens
Spoofed
Reflection
Normal
Reflection
Figure 3. Relaying and spoofing signal attacks on LiDAR.
LiDAR systems in autonomous vehicles can be vulnerable to security attacks, which
can have serious implications for road safety. Here are some examples of how security
assaults on LiDAR sensors can manifest in real-world autonomous vehicles:
Spoofing attacks:
Adversaries can create false signals that mimic the real signals re-
ceived by LiDAR sensors, causing the autonomous vehicle to misinterpret its surroundings.
For example, an attacker could create a fake obstacle close to the front of a victim AV to
cause it to swerve or brake suddenly [32].
Cyber-level attacks:
Attackers can disrupt sensor data by compromising the LiDAR
system, even if they lack situational awareness. This can result in compromised perception
and tracking in multisensor AVs, which can be critical for road safety [35].
Electromagnetic interference (EMI):
LiDAR sensors can be vulnerable to IEMI, which
affects the time-of-flight circuits that make up modern LiDAR systems. This can force
J. Cybersecur. Priv. 2023,3502
the AV perception system to misdetect or misclassify objects and perceive non-existent
obstacles, which can be dangerous for road safety [36].
Adversarial objects:
Attackers can generate adversarial objects that can evade LiDAR-
based detection systems under various conditions. For example, an attacker could create an
object that appears normal to a human observer but is misclassified by the LiDAR system,
causing the AV to make incorrect decisions [37].
To mitigate these vulnerabilities, researchers have proposed various countermeasures,
such as probabilistic data asymmetry monitors and security-aware fusion approaches [
35
].
It is important to take a sensible risk-management approach to tackle potential cyber
security threats to ensure the successful embracing of autonomous vehicles in future
transport systems [38,39].
3.4. The Vulnerability of AV Cameras to Security Attacks
Cameras serve as the optical eyes of autonomous vehicles, providing digital video
feeds of the external world. They are employed in various capacities in AVs, including lane
detection, traffic sign recognition, and headlight detection, among others [40–42].
One significant vulnerability of these camera systems lies in their susceptibility to being
temporarily or permanently obscured by targeted light interference. Such a compromise
could pose a substantial risk to passenger safety, particularly in instances where the vehicle’s
ability to detect essential road signage or traffic signals is undermined [
31
]. Notably, this
has been acknowledged as a potential area of concern by leading industry stakeholders,
with instances recorded of autonomous vehicles, such as those developed by Google,
experiencing difficulties in low-light conditions [43].
A similar form of attack capitalizes on the period of recovery required by cameras after
exposure to high-intensity light. During this interval, the autonomous vehicle may be more
vulnerable to unperceived obstacles. Such an attack could be orchestrated by intermittently
switching a light source on and off and could be initiated from any direction— the front,
back, or side of the vehicle [31].
Several mitigation strategies could be employed to counteract these forms of attacks.
For instance, employing a configuration of multiple cameras, each capturing the same
visual field, could present an added layer of complexity for an attacker aiming to confuse all
cameras at once. Alternatively, the integration of a detachable near-infrared-cut filter could
provide the ability to selectively filter near-infrared light, enhancing the camera’s resilience
to light-based attacks. Moreover, photochromic lenses, with their unique capacity to
change color and block specific light wavelengths, could also serve as a potential protective
measure [31].
In the broader context of emotional intelligence, these threats underscore the need for
autonomous vehicles to possess a degree of perceptual intelligence. This would enable
them to recognize and adjust to potentially harmful inputs, similar to how humans process
and react to threats in their environment. This aspect of emotional intelligence in AVs is
critical for mitigating safety concerns and advancing the technology’s development and
acceptance.
3.5. The Susceptibility of Inertial Measurement Units (IMUs) to Security Attacks
The inertial measurement unit (IMU), an integral part of an autonomous vehicle’s
sensor ecosystem, integrates the functionalities of a gyroscope and an accelerometer. This
combination produces vital information regarding the vehicle’s orientation, acceleration,
and velocity. The IMU also monitors changes in environmental dynamics, such as the
gradient of the road, enabling the vehicle to navigate varied terrains efficiently.
Nevertheless, the IMU is not invulnerable to potential direct security threats. For
example, an attacker could manipulate the sensor data to misrepresent the road’s gradient,
disrupting the autonomous vehicle’s ability to interpret and react to its environment
correctly. Such an attack could lead to significant safety risks, as the vehicle’s behavior
might not align with the actual environmental conditions.
J. Cybersecur. Priv. 2023,3503
This potential threat was exemplified by the work reported in [
44
], where the authors
developed the CarShark tool to monitor data flow in the vehicle’s controller area network
(CAN) bus system. Thorough packet analysis and modification, the tool enabled the
simulation of a man-in-the-middle attack on the CAN network. By altering the data packet,
they could manipulate the sensor’s readings, demonstrating the potential implications of
such an attack on autonomous vehicles.
A variety of countermeasures might help mitigate such a security threat. One potential
strategy involves implementing encrypted communication within the vehicle’s network,
adding a layer of protection against unauthorized data manipulation. Another approach
could be the use of additional, redundant sensors to provide backup measurements. This
could involve using GPS data to verify the vehicle’s inclination, providing an extra check
against erroneous readings from a compromised IMU.
In the context of the emphasis of this article on emotional intelligence, it is essential to
extend this concept to how a self-driving vehicle interprets and responds to sensor data.
Similar to how humans use emotional intelligence to traverse complex social situations, an
autonomous vehicle could use perceptual intelligence to recognize and respond adequately
to potential hazards to its sensor inputs. This additional intelligence could considerably
contribute to the overall safety and dependability of autonomous vehicles while also
opening up new research avenues in the field.
4. Cyber Security Attacks in Vehicular Ad Hoc Networks (VANETs)
Autonomous vehicles (AVs) operate utilizing two principal communication channels,
namely vehicle-to-vehicle (V2V) and Vehicle-to-infrastructure (V2I) communications, which
are critical components of the broader Vehicular ad hoc network (VANET) ecosystem. A
detailed analysis of the structure and operations of a VANET is depicted in Figure 4.
The V2V communication paradigm leverages the principles of peer-to-peer network-
ing, enabling vehicles to establish mutual connections. This system is underpinned by
the IEEE 802.11p protocol and is designed based on the assumption that vehicles within
a specified range of radio communication can automatically form an ad hoc network.
Within this network, nodes represented by the vehicles can share vital data points such
as positional coordinates, speed metrics, directional vectors, and more. Combined with
V2V, the V2I communication mechanism allows vehicles to establish connections with
embedded electronic devices in the broader transportation infrastructure. The information
exchanged between vehicles and infrastructure can be utilized for various applications,
including enhancing traffic management, optimizing traffic flow, promoting fuel efficiency,
and reducing environmental impact.
Vehicular communication systems, underpinned by the IEEE 802.11p protocol, have
emerged as a transformative force for improving road safety and traffic efficiency [
45
].
These systems leverage vehicle-to-vehicle (V2V) communication for sharing of pivotal
data points such as positional coordinates and speed metrics, and vehicle-to-infrastructure
(V2I) communication to interface with the embedded electronic devices in transportation
infrastructure. However, the effectiveness of these communication methods relies heavily
on the accuracy of wireless channel estimates, such as channel state information (CSI) and
received signal strength. Consequently, researchers have been mobilizing efforts towards
the development of deep-learning-based channel prediction algorithms and conducting
measurement campaigns to generate reliable wireless channel estimates [45].
Simultaneously, road traffic safety remains a vital concern in the domain of vehicular
communication. Mehdizadeh et al. highlighted the development of predictive models
to gauge crash risks based on varying driving conditions and the implementation of
optimization techniques such as path selection and rest–break scheduling to augment road
safety [
46
]. However, bridging the gap between research outcomes and real-time crash
risk optimization remains a challenge. Complementing safety, security issues have gained
attention due to the highly active and ever-changing topology of vehicular environments.
Proposals for security models grounded in evolutionary game theory aim to identify
J. Cybersecur. Priv. 2023,3504
common attacks and establish defenses [
47
]. Concurrently, the rise in smart car sensors and
applications reliant on artificial intelligence and augmented reality has escalated challenges
related to computational resources and latency requirements. Efforts are underway to
advance secure multiaccess edge computing and intelligent vehicle control systems to
meet these demands [
48
]. Overall, vehicular communication systems present an array
of opportunities alongside a spectrum of challenges, underscoring the need for ongoing
research and innovation.
Central
Data Server
Edge
Data Server
Smart City Ecosystem
V2I
V2V V2I
V2I
V2P
Figure 4. VANET.
4.1. Autonomous Vehicles: Security Measures and Technological Interplay
Artificial intelligence (AI) and machine learning (ML) technologies have empowered
the development and efficiency of autonomous vehicles, catalyzing intelligent decision
making and improved security. However, just like vehicle ad hoc networks (VANETs),
autonomous vehicles are also vulnerable to various cyber threats. In particular, these
vehicles integrate technologies like LiDAR, Radar, GPS, and computer vision, which also
become potential targets for cyber attacks. Notable threats include Sybil and replication
attacks, which involve spoofing and identity theft, capable of causing critical operational
issues [49,50]
. Countermeasures against these threats, some of which were initially de-
veloped for VANETs, have been adapted for autonomous vehicles. For instance, digital
signatures and time stamps help authenticate messages and data sources [
51
,
52
]. Mech-
anisms that detect disparities in motion trajectories and, hence, identify potential Sybil
attacks, can also be applied in the context of autonomous vehicles [51].
Additionally, Hao et al.
[53]
presented a cooperative message authentication protocol
(CMAP), which includes the sender vehicle’s location data in each safety message. This
protocol can be adopted in autonomous vehicles to counter replication attacks, thus sig-
nificantly improving security [
53
]. Furthermore, techniques such as the anonymous batch
authenticated and key agreement (ABAKA) scheme, which was initially introduced in
J. Cybersecur. Priv. 2023,3505
VANETs [
50
], can be tailored for autonomous vehicles to authenticate multiple requests
and generate multiple session keys simultaneously. It aids in quick validation and session
key negotiation, thus minimizing transmission overhead and verification latency.
To tackle the evolving security landscape, the development of advanced intrusion de-
tection systems (IDS) is crucial. By leveraging anomaly detection techniques, autonomous
vehicles can quickly detect and react to deviations from normal operations triggered by
potential cyber attacks. Finally, secure communication protocols can be incorporated into
the vehicle’s systems to safeguard sensitive data, thereby further improving the overall
security of autonomous vehicles [54–56].
4.2. Availability Attacks, Benefits, and Security Solutions in Autonomous Vehicles
Ensuring system availability is a crucial aspect of the security framework for au-
tonomous vehicles. Autonomous vehicles, like vehicular ad hoc networks (VANETs), face
threats such as denial of service (DoS) attacks, which can exhaust network resources and
disrupt vehicle operations. Effective countermeasures include authentication measures,
anomaly detection systems, and cryptographic solutions.
Malware attacks pose a significant risk, as they involve the deployment of malicious
software, such as computer viruses, which can compromise the software infrastructure of
autonomous vehicles. To protect against such threats, firewall technologies and antimal-
ware systems are instrumental [57].
Denial of service (DoS) attacks aimed at blocking legitimate entities from accessing
resources and services are another significant threat. A more pervasive form of DoS attack is
the distributed denial-of-service (DDoS) attack, which utilizes multiple computing devices
or Internet connections. These attacks can severely impact the functionality and service
availability of autonomous vehicles [58–60].
The benefits associated with addressing availability attacks and implementing the
related countermeasures are manifold:
•
Robust operational stability: Effective countermeasures against DoS and DDoS attacks
can ensure that autonomous vehicles continue to operate without disruption. This
enhances the reliability and robustness of autonomous vehicles in various driving con-
ditions.
•
Enhanced security infrastructure: Utilizing robust firewall technologies and antimal-
ware systems not only shields autonomous vehicles from malware attacks but also
significantly fortifies the vehicle’s overall security infrastructure.
•
Improved user trust and confidence: As autonomous vehicles become more secure and
less susceptible to attacks, user trust and confidence in this technology can be expected
to increase. This can facilitate the broader acceptance and adoption of autonomous
vehicles.
•
Prevention of potential misuse: Effective countermeasures can prevent potential
misuse of autonomous vehicles, such as using them as nodes in distributed denial-of-
service attacks.
•
Guaranteed service accessibility: By effectively mitigating DoS and DDoS attacks, the
continuous availability of essential vehicle services and resources can be guaranteed,
which is crucial for the functionality of autonomous vehicles.
•
Accurate vehicle routing: By mitigating wormhole attacks, accurate distance calcula-
tions between nodes, which are essential for precise vehicle routing, can be ensured.
•
Versatility and performance: Techniques like HEAP offer a versatile solution that
works across various applications and provides superior performance, making them
well-suited for autonomous vehicles.
Incorporating these countermeasures into the design and operation of autonomous
vehicles provides a significant benefit by increasing their resilience against cyber attacks
and thus ensuring their reliable and secure operation. As a result, the safety and efficiency
of these vehicles are significantly improved, contributing to the broader aim of safe and
reliable autonomous driving.
J. Cybersecur. Priv. 2023,3506
He et al. proposed a preauthentication solution to counteract DoS attacks, which uses
a group rekeying mechanism, along with a one-way hash chain [
60
]. Additionally, Verma
et al. suggested a method for packet filtering and abrupt change detection to prevent DoS
attacks in autonomous vehicles [
61
]. With the development of these countermeasures, it
can be inferred that DoS attacks have been considerably mitigated.
A wormhole attack, which involves transporting packets from one network segment
to another, can significantly disrupt routing algorithms in autonomous vehicles that rely on
accurate distance calculations between nodes [
61
]. A countermeasure proposed by Safi et
al. restricts the maximum transmission range of packets, ensuring that the received packet
is within a practical range of the sender. This method, known as HEAP, provides superior
performance compared to other authentication techniques, making it particularly suitable
for autonomous vehicles. HEAP can be utilized across unicast, multicast, and broadcast
applications and can authenticate all types of packets [62].
In conclusion, addressing availability threats in autonomous vehicles is paramount for
ensuring their functionality and safety. The application of robust defensive strategies and
countermeasures against these attacks is essential for safeguarding these vehicles against
potential threats.
4.3. Data Integrity Attacks in Autonomous Vehicles
Data integrity is pivotal in autonomous vehicle systems, forming the bedrock of
vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications. It is crucial
for everything from navigation to collision avoidance [
63
]. However, the risk of data
manipulation is omnipresent, either through unauthorized intrusion or malicious intent.
To preserve data integrity, autonomous vehicles employ secure communication protocols
and advanced encryption methods.
One commonly encountered breach is the masquerading attack, where the perpetrator
impersonates a legitimate entity within the system. This can cause traffic disruption, trigger
accidents, or potentially allow the attacker to control vehicle systems or extract sensitive
data [
39
]. Therefore, autonomous vehicles must incorporate systems to authenticate the
received information or signals.
Addressing these challenges, T.W. Chim et al. introduced the secure and privacy-
enhancing communications schemes (SPECS) system [
64
]. SPECS leverages pseudo iden-
tification and a shared secret key between an autonomous vehicle and roadside units
(RSUs) to maintain the vehicle’s identity confidentiality. Even when pseudonyms are
used for communications, RSUs can verify the signatures, thus preventing masquerading
attacks [63,64].
Another potential breach of data integrity is represented by the replay attack or
playback attack. This attack involves the malicious repetition or delay of valid data trans-
missions [
39
]. Modern security designs using robust cryptographic systems, including
digital signatures and nonce inclusion in messages, are typically effective at countering
these attacks.
However, the challenge is amplified when the attacker is a trusted insider, like a
compromised vehicle with a valid certificate. Anomaly or misbehavior detection systems,
such as those discussed in [
65
], are often employed to manage these scenarios. However,
these systems have inherent limitations concerning false negatives and positives and rely
on a variety of data sources that might not always be available. Consequently, further
research is required in anomaly detection to ensure the robust operation of these algorithms
and the consequent protection of autonomous vehicles.
4.4. Threats to Confidentiality in Emotionally Intelligent Autonomous Vehicles
Confidentiality attacks, although not perceived as the most significant threats to
vehicular system security, nevertheless constitute considerable privacy risks. Preserving
the confidentiality of data transmitted within vehicular networks, such as the geographical
location of autonomous vehicles, intelligent transport systems (ITS) safety alerts, and
J. Cybersecur. Priv. 2023,3507
driver information, is paramount. Possible data breaches can be deterred through secure
transmission techniques and fortified data encryption protocols.
An illustrative confidentiality threat in the realm of vehicular networks is the eaves-
dropping attack. During standard vehicle operation, vehicles frequently broadcast beacon
signals encapsulating a wealth of data, such as the vehicle’s identity, current location,
velocity, and acceleration. This disseminated information is vulnerable to unauthorized
interception, potentially breaching the privacy of the system. An adversary armed with
the appropriate tools could execute an eavesdropping attack, harvesting precious data
from the transmitted information. By associating this extracted location data over time, the
adversary could track the vehicle’s path and possibly manipulate it for malicious activities.
Given its passive nature, eavesdropping presents a formidable challenge, as its detec-
tion is difficult, especially in a broadcast wireless communication context. Nonetheless,
the successful orchestration of eavesdropping can be thwarted by employing data encryp-
tion protocols to shield data privacy or anonymization techniques to protect identity and
location data. Anonymity is generally realized using group signatures [
66
] or temporary
certificates, also known as pseudonyms [67].
In the context of emotionally intelligent autonomous vehicles, confidentiality attacks
bear an additional layer of intricacy and risk. “Emotionally intelligent autonomous vehicles”
refer to autonomous vehicles that are equipped with the ability to perceive, comprehend,
and appropriately react to human emotions. These vehicles utilize a range of technologies,
such as AI, ML, and sensor technologies, complemented by effective computing capabili-
ties. They can identify and respond to emotional cues by recognizing facial expressions,
interpreting voice sentiments, processing physiological signals, and more. Their goal is
to enhance the passengers’ comfort, safety, and overall experience by adapting to their
emotional states.
The emotional data in these vehicles represent a core component of their intelligent
system and are highly personal and sensitive. Thus, the implications of an eavesdropping
attack could extend beyond mere vehicle tracking, potentially leading to unauthorized ac-
cess and exploitation of a passenger’s emotional data. To effectively safeguard this sensitive
information, advanced encryption, and anonymization techniques must be amalgamated
within the system’s data communication protocol. This integration should ensure that
even in the event of an eavesdropping attack, the intercepted data would not be useful to
the adversary.
Moreover, emotionally intelligent autonomous vehicles may necessitate additional
privacy protection layers due to the nature of the processed data. For instance, homomor-
phic encryption could be employed to allow the system to analyze and react to emotional
data without ever needing to decrypt them, thereby maintaining privacy. Additionally,
differential privacy concepts could be applied to ensure that the system’s responses do not
inadvertently disclose sensitive information about the passengers’ emotional states. These
advanced methods could form a vital part of a comprehensive strategy for preserving data
confidentiality in emotionally intelligent autonomous vehicles.
4.5. Vehicle-to-Pedestrian (V2P) Network and Its Implications
As we navigate an increasingly digitized world, the ubiquity of smartphone usage
among both drivers and vulnerable road users (VRUs) has grown exponentially. To mitigate
the potential hazards associated with this trend, particularly in contexts where traffic is a
primary concern, it is essential to develop novel approaches for enhancing road safety. One
such strategy involves the implementation of collision prediction algorithms, capitalizing
on pedestrian-to-vehicle (P2V) and vehicle-to-pedestrian (V2P) communication technolo-
gies [68]. A. Hussein et al. presented a sophisticated example of this approach, proposing
an algorithm that uses global positioning system (GPS) data and magnetometer readings
from a pedestrian’s smartphone in conjunction with sensor data from an autonomous
vehicle to predict potential collision scenarios.
J. Cybersecur. Priv. 2023,3508
Figure 5provides a schematic representation of this innovative algorithm’s function-
ality. It highlights the method by which the algorithm calculates the potential angle of
collision, thereby informing preventative measures.
V2
(X2 ,Y2)
Θ2
V1
(X1 ,Y1)
Θ1
(Xc , Yc)
Figure 5. Schematic representation of the collision prediction algorithm.
Upon the prediction of a possible collision, the system alerts both the pedestrian
and the autonomous vehicle, enabling them to take necessary evasive action and replan
their routes accordingly. This immediate response facilitates the prevention of accidents,
enhancing the safety of all road users.
While V2P communication is an emerging area of exploration, its potential for improv-
ing road safety cannot be underestimated. Nonetheless, given its nascent stage, it requires
substantial research and development to ensure its robustness and security. Future aspira-
tions of achieving a fully integrated Internet of Things (IoT) are reliant on the resolution of
current security vulnerabilities. As such, it is crucial that further advancements in this field
be underpinned by an unwavering commitment to ensuring the security and safety of all
connected devices. By doing so, we can progress towards realizing the full potential of V2P
communications, contributing significantly to the development of emotionally intelligent
autonomous vehicles.
5. Autonomous Vehicle Vulnerabilities
Autonomous vehicles, despite their potential to revolutionize transportation, are not
immune to the growing complexity of cyber threats due to their dependence on advanced
digital technologies [
69
]. Potential hackers may exploit these cyber vulnerabilities, creating
a threat to the safety and privacy of individuals, regardless of whether they are motivated
by malicious goals or simple curiosity [
69
]. Detailed analyses of these cyber threats can be
discovered on informative platforms such as HackerNoon [70].
The following sections highlight some of the prominent attack vectors that could
potentially compromise the integrity of autonomous vehicles:
•
Key fob hacking: Remote vehicle access and ignition made possible by key fob tech-
nology can be compromised. By using devices to strengthen the signal transmitted by
a vehicle’s key transponder, hackers can gain unauthorized entry and even remotely
start the vehicle [71].
J. Cybersecur. Priv. 2023,3509
•
Control area network (CAN) bus attacks: The CAN bus, which operates as the electrical
network that connects the various electronic control units (ECUs) within a vehicle, is
an attractive target for hackers. By exploiting vulnerabilities in the CAN bus, hackers
can take control of fundamental vehicle functions such as braking and steering [71].
•
Entertainment system hacking: Given its connection to the CAN bus, a vehicle’s
entertainment system can provide a back door for hackers, which, once breached,
could potentially grant a hacker full control over the vehicle’s systems [71].
•
Adversarial machine learning techniques: Autonomous vehicles rely heavily on ma-
chine learning algorithms to interpret sensor data and make operational decisions.
By employing adversarial machine learning techniques, such as evasion or poisoning
attacks, hackers can manipulate sensor data, causing the vehicle to make faulty and
potentially hazardous decisions [72].
•
User data theft: Considering the plethora of user data stored in autonomous vehicles,
these vehicles become prime targets for cyber criminals. A hacked vehicle can lead
to significant privacy violations and impose safety risks to the driver and others on
the road.
•
Remote vehicle hijacking: In a potentially dangerous scenario, hackers might gain
remote control of a self-driving car, causing passengers to experience difficulty.
•
Denial-of-service (DoS) attacks: The launching of DoS attacks against the vehicle’s
systems could lead to system shutdown or failure.
The extensive range of potential attack vectors underscores the imperative to enhance
cyber security measures in autonomous vehicles. A compromised vehicle becomes a signif-
icant threat to all road users, necessitating immediate rectification of these vulnerabilities
for public safety [
69
]. To fully understand the scope of these vulnerabilities, it is necessary
to delve into the hardware aspects, specifically focusing on critical components such as the
onboard diagnostic port (OBD) and the engine control unit (ECU).
5.1. Hardware Vulnerabilities: Onboard Diagnostic Port (OBD)
The onboard diagnostic (OBD) port, a critical component in modern vehicles, provides
a critical gateway to gathering diagnostic information. These vital data include an array of
performance metrics and potential system faults, which the port communicates directly
to the electronic control unit (ECU) via the controller area network (CAN) bus. Typically,
the OBD port interface is a compact device akin to a standard USB drive, usually situated
beneath the dashboard near the driver’s seat. This interface can be connected to external
computational devices, either via physical tethering or wireless connectivity, facilitating
bidirectional data transfer between the vehicle’s ECU and the connected device.
This communication system can have various applications. For example, it can be used
to orchestrate cars as discussed in [
73
] or to analyze the effects of an onboard unit on the
driving behavior of cars in connected vehicle flow, as detailed in [
74
]. With the emergence
of advanced technologies, the OBD port can also be leveraged for more sophisticated tasks,
such as suppressing selfish node attack motivation in vehicular ad hoc networks through
deep reinforcement learning and blockchain, as proposed in [
75
], or assessing the trust level
of a driverless car using deep learning techniques, as investigated by [
76
]. These innovative
applications highlight the critical role of the OBD port in enhancing the functionality and
safety of autonomous vehicles.
Zhang et al.
[77]
, in their work, illustrated the vulnerabilities inherent in this system by
successfully breaching multiple automobile models through the OBD port. The potential
consequences of such breaches are severe, extending to remote control of the vehicles,
highlighting the urgent need for robust security measures within OBD port systems.
Once an external device is linked to the OBD, it gains the ability to send and receive
data to and from the vehicle’s ECUs. Such an open connection can be exploited to introduce
malicious payloads into vehicle networks. This threat was further underscored by W. Yan’s
research [
78
], which demonstrated the feasibility of manipulating data packets to initiate
such attacks. In addition to posing a direct threat to vehicle operations, such vulnerabilities
J. Cybersecur. Priv. 2023,3510
also carry potential ramifications for intellectual property theft. Criminal organizations
could leverage these security gaps to steal proprietary information relating to suppliers’
and original equipment manufacturers’ (OEM) production processes. This not only enables
the production of counterfeit parts but also breaches driver privacy by exposing sensitive
information such as driving habits.
In response to these threats, countermeasures have been proposed by various re-
searchers. Yadav et al.
[79]
introduced a defense mechanism that combines the seed key
protocol with a two-way authentication method and a timer method. This approach seeks
to enhance security by making the seed and key values more difficult to decrypt. Like-
wise, Oka and Larson
[80]
proposed the use of cryptographic techniques to authenticate
messages on the CAN, thus limiting the transmission of unauthorized data.
However, despite these efforts, a comprehensive and foolproof solution to secure OBD
port systems remains elusive. This area of research is still in its first steps and requires
further in-depth investigation. This is particularly true in the context of emotionally
intelligent autonomous vehicles, where ensuring the security of the OBD port system is
fundamental to maintaining the operational integrity of these vehicles and protecting the
privacy of their users.
5.2. Hardware Vulnerabilities: Engine Control Unit (ECU)
The engine control unit (ECU) plays a vital role in the orchestration of a vehicle’s
functionalities, acting as the central processing entity for a range of control functions within
an automotive system. By interpreting, analyzing, and managing a myriad of electronic
signals, the ECU oversees critical operational aspects of vehicles, including fundamental
features such as the core braking system.
Various studies have underscored the vulnerability of ECUs to sophisticated infil-
tration strategies. The work of Vallance
[81]
is particularly instructive, revealing how
intruders can exploit the onboard digital audio broadcasting radio as an entry point to gain
unauthorized access to ECUs. Upon breaching this boundary, malicious attackers have
the capacity to manipulate the CAN (controller area network), a critical communication
highway that interconnects different vehicular subsystems. Such disruptions can have
profound implications, potentially compromising the core functionalities of the vehicle and
thereby posing significant safety risks.
The potential gravity of such security breaches is further underscored by the observa-
tions of Checkoway et al.
[82]
. Their findings indicate that the security measures currently
implemented in ECUs are often insufficient in thwarting attempts at unauthorized firmware
access or modification. Given that firmware alterations have the potential to completely
reprogram a vehicle’s behavior, this vulnerability is of significant concern in the context of
public safety. However, their research is not only diagnostic but also offers a path toward
remediation. They propose the adoption of an asymmetric cryptographic framework rooted
in the use of public–private key pairings. This approach helps to ensure that any firmware
introduced to the system originates from a verified and trusted source. In this way, the
risk of unauthorized firmware modifications can be considerably mitigated, helping to
safeguard against malicious intent.
Despite this, guaranteeing the robustness of ECU security is a difficult task, given
the complexity and evolution of cyber security threats. Not only is the scope of possible
infiltration techniques a challenge, but so is the depth of possible exploits once access has
been gained. The discovery of these latent vulnerabilities requires exhaustive testing and
evaluation, utilizing both proven cryptographic techniques and emergent cyber security
methodologies.
5.3. Countermeasures for Autonomous Vehicles
The classification of various types of attacks on autonomous vehicles and their re-
spective countermeasures are shown in Table 2. In this table, we emphasize the scope
and complexity of the threats that autonomous vehicles face. Although significant strides
J. Cybersecur. Priv. 2023,3511
have been made in countering some types of attacks, a number of vulnerabilities, such
as those associated with OBD port tampering, man-in-the-middle attacks on the CAN
bus, and replay attacks by insider adversaries, remain unaddressed. A comprehensive
understanding and solution for these vulnerabilities are crucial to realize the full potential
of emotionally intelligent autonomous vehicles.
Table 2. Data Security attacks and mitigation strategies in autonomous vehicles.
Attack Type Specific Attack Proposed Countermeasures Ref. Mitigation Status
Data Security
Data Authenticity
Signal strength monitoring [28] Partially mitigated
Military-grade cryptography [28] Partially mitigated
Antispoofing methods [29] Partially mitigated
Man-In-The-Middle Cryptography on CAN [44] Not addressed
Secondary measurement source [44] Not addressed
Data Availability
GPS jamming [28] Partially mitigated
Antijamming methods [29] Partially mitigated
LiDAR jamming [31] Fully mitigated
Use of alternate data sources [31] Fully mitigated
Camera blinding [31] Fully mitigated
Malware attack [83] Partially mitigated
DoS/DDoS attack [60,61] Fully mitigated
Data Integrity
Replay attack on LiDAR [31] Fully mitigated
Auto control confusion [31] Fully mitigated
OBD port tampering [84] Not addressed
Exploitation and injection in CAN bus [79,85] Not addressed
Masquerading attack [64] Fully mitigated
Replay attack (outsider adversary) [39] Fully mitigated
Replay attack (insider adversary) [65] Not addressed
Data Confidentiality
Eavesdropping Cryptographic solution (group signatures) [66] Partially mitigated
Eavesdropping Cryptographic solution (short-term
certificates) [67] Partially mitigated
Autonomous vehicles (AVs) introduce numerous unique security challenges that have
the potential to create safety consequences on the road [
86
]. Therefore, security measures
are paramount to the implementation of AV networks [
87
]. Here are some countermeasures
being developed and deployed to solve the security flaws of autonomous vehicles:
•
Game-theory-based solutions: Game-theory-based solutions could offer resilience in
the context of AVs [
88
]. Game theory is a mathematical framework for modelling
decision-making in situations where multiple parties have conflicting interests. It can
be used to model the behavior of attackers and defenders in a cyber security context
and to develop optimal strategies for both parties [88].
•
Benchmarking frameworks: AVs lack a proper benchmarking framework to evaluate
attack and defense mechanisms and quantify safety measures [
86
]. BenchAV is a
security benchmark suite and evaluation framework for AVs that addresses current
limitations and pressing challenges of AD security. The benchmark suite contains
12 security and performance metrics and an evaluation framework that automates
the metric collection process using the Carla simulator and robot operating system
(ROS) [86].
J. Cybersecur. Priv. 2023,3512
•
Opportunistic networking protocols: Novel networking protocols in vehicular ad hoc
networks (VANETs) are being developed to provide data to autonomous trams and
buses in a smart city [
87
]. Opportunistic networking protocols are used to bridge the
gap between fully distributed vehicular networks based on short-range vehicle-to-
vehicle communication and cellular-based infrastructure for centralized solutions [
87
].
The state-of-the-art MaxProp algorithm outperforms the benchmark, as well as other
more complex routing protocols, in most of the categories [87].
•
Blockchain-based architecture: A blockchain-based architecture could provide a
promising solution to the threats of cyber attacks that jeopardize the security and con-
nectivity of CAVs [
89
]. Blockchain technology can be used to secure communication
between vehicles and infrastructure and to ensure the integrity and confidentiality
of data [
89
]. It can also be used to provide a secure and decentralized platform for
vehicle-to-vehicle and vehicle-to-infrastructure communication [89].
These countermeasures aim to address the security challenges facing AVs, including
trustworthiness, security, safety, and complexity. By developing and deploying these
solutions, AVs can become more reliable and safe, which is crucial for the adoption of such
technology in smart projects [88].
Specific cyber security measures and examples of security solutions implemented in
autonomous vehicles include:
•
Secure communication protocols: Implementing secure communication protocols,
such as transport-layer security (TLS), can help protect the vehicle’s connectivity and
prevent unauthorized access to the vehicle’s network [90].
•
Encryption: Utilizing encryption techniques can safeguard the data transmitted be-
tween different components of the autonomous vehicle system, including sensors,
controllers, and communication interfaces [91].
•
Intrusion Detection systems (IDS): IDS can be deployed in autonomous vehicles to
monitor the network and detect any suspicious or malicious activities. IDS can identify
potential cyber attacks and trigger appropriate responses to mitigate the risks [92].
•
Secure over-the-air (OTA) updates: OTA updates allow for the remote updating of
software and firmware in autonomous vehicles. Implementing secure OTA mecha-
nisms ensures that updates are authenticated, encrypted, and tamper-proof, reducing
the risk of unauthorized modifications or malware injection [91].
•
Access control and authentication: Implementing strong access control mechanisms
and multifactor authentication can prevent unauthorized access to critical vehicle
systems. This includes securing access to the vehicle’s control systems, sensors, and
communication interfaces [90].
•
Secure sensor fusion: Sensor fusion is crucial for perception in autonomous vehicles.
Ensuring the security of sensor data fusion is important to prevent the injection of
false or manipulated sensor data, which could lead to incorrect decisions by the
autonomous system [93].
•
Behavioral anomaly detection: Implementing behavioral anomaly detection tech-
niques can help identify abnormal behavior in the vehicle’s network or system, indi-
cating a potential cyber attack. This can include monitoring network traffic patterns,
system performance, and sensor data consistency [94].
•
Redundancy and fail-safe mechanisms: Building redundancy and fail-safe mecha-
nisms into autonomous vehicle systems can help mitigate the impact of cyber attacks.
This includes redundant sensors, controllers, and communication channels to ensure
that the vehicle can still operate safely, even if one component is compromised [93].
•
Continuous monitoring and updates: Regularly monitoring the security of the au-
tonomous vehicle system and applying security updates and patches is essential to ad-
dress newly discovered vulnerabilities and protect against emerging cyber threats [
92
].
These cyber security measures are crucial to ensure the safety and integrity of au-
tonomous vehicles in the face of potential cyber attacks. By implementing these measures,
J. Cybersecur. Priv. 2023,3513
manufacturers and researchers aim to minimize the risks associated with autonomous
vehicle cyber security and enhance the overall security posture of these vehicles.
Current Status and Future Directions of Sensor Security in Autonomous Vehicles
As the complexity and ubiquity of autonomous vehicles (AVs) continue to increase, the
current state of their sensor security and future directions for research become critical topics.
The current landscape of AV sensor security involves a plethora of techniques developed to
counteract the broad spectrum of potential threats. These techniques encompass a multitude
of disciplines, such as encryption, anomaly detection, intrusion detection systems, and
secure communication protocols.
The field of encryption, for example, is being utilized to protect data in transit from
sensors to the vehicles’ central processing units and cloud-based systems. Secure commu-
nication protocols, such as transport layer security (TLS) and Internet protocol security
(IPSec), are being employed to ensure that data communicated between vehicles and infras-
tructure are authenticated and encrypted. Moreover, techniques from the realm of machine
learning and data science are being incorporated to develop sophisticated anomaly de-
tection systems that can identify unusual patterns in sensor data that are indicative of a
potential attack.
Despite these advancements, the industry faces considerable challenges that need to
be addressed to increase AVs’ resilience against threats. First, due to the complex nature of
AVs and the environments they operate within, creating a comprehensive and foolproof
security solution is inherently difficult. Additionally, the rapidly evolving nature of cyber
threats necessitates constant vigilance and adaptation in the field of AV sensor security.
The future of AV sensor security research holds promising directions. Enhancing
machine learning algorithms to detect subtle and complex attacks, developing more so-
phisticated encryption techniques, and integrating blockchain technology for secure data
recording and transaction are some of the notable future directions. However, a critical part
of the future lies in developing security measures that are proactive rather than reactive,
preventing attacks before they occur rather than responding to them post event.
Furthermore, it is essential to ensure that security measures do not impede the func-
tionality and efficiency of AVs. Achieving this balance will require advancements in both
hardware and software, necessitating ongoing research and development. In summary,
the current status of sensor security in AVs presents a dynamic landscape fraught with
challenges and opportunities. The future is ripe for research that addresses these challenges
head-on, advancing the safety and security of AVs.
6. Vulnerabilities of Deep Neural Networks in the Face of Adversarial Machine
Learning: Implications for Autonomous Vehicles
The field of adversarial machine learning has demonstrated that it is possible to exploit
the weaknesses that are built into deep neural networks (DNNs). This potential has been
confirmed in state-of-the-art research. The first adversarial machine learning attacks on
DNNs were proven in a significant study that was conducted by Szegedy et al.
[95]
. During
the experimental phase, the researchers introduced the idea of adversarial instances, which
are small modifications made to the input images. These modifications have the potential
to influence DNNs and result in inaccurate categorization. This method, which is based
on a gradient-based attack, devises adversarial instances that are only slightly different
from the original. By tricking image classifiers with these inputs that had been deceptively
manipulated, the method was successful.
In response to this groundbreaking work, Goodfellow et al.
[96]
formulated adversarial
machine learning as a min–max problem and developed an alternative gradient-based
method. This technique now commonly referred to as the fast gradient sign method
(FGSM) was an effective tool for generating adversarial instances. The researchers also
introduced adversarial training, a technique used to fortify DNNs against such adversarial
instances, advancing our knowledge of both the vulnerabilities and potential defenses in
J. Cybersecur. Priv. 2023,3514
this field. The vulnerabilities of DNNs were further highlighted by andIan Goodfellow and
Bengio
[97]
. Utilizing adversarial examples derived from images taken by a mobile phone
camera, they revealed the susceptibility of machine learning (ML) and deep learning (DL)
techniques to attacks in real-world scenarios. Using the basic iterative method (BIM), a
more sophisticated version of FGSM, they produced adversarial examples that successfully
deceived advanced image classifiers.
Further research [
98
] highlighted the susceptibility of convolutional neural networks
(CNNs), the most sophisticated deep-learning-based image categorization algorithms,
to simple manipulations such as rotations and translations. Another study [
99
] empha-
sized this vulnerability, demonstrating that even basic geometric transformations like
translation, rotation, and blurring could confound ten state-of-the-art CNNs. Meanwhile,
Liu et al. [100]
offered a different approach by modifying the neurons of an already trained
model, demonstrating a stealthy back-door attack on the model. The researchers mali-
ciously injected the ML model and applied it to an autonomous vehicle, where a specific
trigger, a particular billboard in this case, caused the vehicle to behave unpredictably.
With a different strategy, Papernot et al.
[101]
constructed a white-box Jacobian
saliency-based adversarial attack (JSMA), which manipulated the mapping between the
input and output of DNNs to deceive the classifiers. They also proposed a defensive tech-
nique, namely defensive distillation, where a model is trained to predict the probabilities
of another model trained on the baseline standard. This aimed to foster a higher emphasis
on accuracy, serving as protection against adversarial perturbations.
Papernot et al. [102]
extended their work to present a black-box adversarial ML attack that exploited the transfer-
ability property of adversarial examples. This method not only deceived ML/DL classifiers
but also bypassed the defensive distillation mechanism, highlighting the sophistication
of adversarial attacks. Carlini and Wagner
[103]
introduced a synergy of adversarial tech-
niques known as C&W attacks, utilizing three distinct distance metrics: L1, L2, and L
∞
.
These attacks were successful in evading both defensive distillation and DNN classifiers,
exposing the inadequacy of existing defensive strategies. In another study Carlini and
Wagner
[104]
demonstrated that their adversarial attacks could bypass ten commonly used
defensive techniques against adversarial instances, further emphasizing the complexity of
the problem.
Another alarming vulnerability was demonstrated by Brown et al.
[105]
, who reported
that a malicious patch, when applied to an original image, led the deep model to misclassify
that image. These universal adversarial patches could deceive classifiers without the need
to know about other objects present in the image, allowing for the offline creation and
dissemination of such patches. Su et al.
[106]
adopted an alternative approach by employing
differential evolution to generate one-pixel adversarial perturbations. This novel attack
demonstrated the capability of minimal yet calculated manipulations to compromise a
variety of neural networks.
Despite substantial progress in identifying and comprehending these weaknesses,
effective defense mechanisms against adversarial attacks remain elusive, while some
countermeasures have proven to be somewhat effective against low-level attacks, they
are ineffective against a broader range of sophisticated attacks. In order to ensure the
security and dependability of autonomous vehicles in the context of adversarial ML attacks,
it is crucial that future research focuses on the development of more robust defensive
strategies. This critical requirement highlights the scale of the challenge and the significance
of ongoing research in this crucial area in order to ultimately secure autonomous vehicles
against malicious threats.
7. Big Data in Autonomous Vehicles
The use of large amounts of data is crucial to the successful operation of autonomous
vehicles from both a safety and productivity perspective. The list that follows is an overview
of crucial details discovered as a result of our study:
J. Cybersecur. Priv. 2023,3515
•Security Challenges due to big data utilization in AVs:
As autonomous vehicles
(AVs) increasingly exploit big data to enhance their operations, they are simultaneously
exposed to a range of security concerns, including cyber security vulnerabilities. One
such difficulty is that autonomous vehicles (AVs) are more vulnerable to cyber-attacks.
According to [
71
], the nature of these vulnerabilities is multifaceted, including data
breaches, illegal access, and the manipulation of vehicle sensor data. According to the
findings of this study, in order to further enhance the safety of the utilization of big
data applications in autonomous vehicles, it is essential to create complex encryption
strategies and to create algorithms based on machine learning for anomaly detection.
•Designing robust path-following functionality through big data analysis:
The abil-
ity of autonomous vehicles to navigate predetermined routes in a manner that is both
secure and economical is one of the most important characteristics of these vehicles.
According to [
107
], the processing of a significant amount of data on road conditions,
vehicle dynamics, and environmental factors could potentially make it feasible to
perform big data analysis by simplifying the process for scientists to develop robust
path-following algorithms. By synthesizing these data, autonomous vehicles have
the ability to generate appropriate motion profiles and dynamically change velocities.
This ensures that tire–road contact remains intact under a variety of circumstances,
which leads to a higher standard of safety.
•Environmental sensing through numerous sensing modalities:
In order for au-
tonomous cars to be able to make intelligent decisions, it is crucial for these vehicles to
have an extended understanding of their surroundings. As indicated by the research
presented in [
108
], autonomous vehicles are equipped with a variety of sensors, in-
cluding LiDAR, radar, ultrasonic sensors, and cameras, which continuously collect
high-dimensional data on their surrounding area. Big data analytics could potentially
be utilized for analysis of these sensor data in real-time, granting the vehicle the ability
to generate a high-definition image of its surrounding environment, which is vital for
both navigation and the avoidance of obstacles.
•Interconnected vehicle platform for enhanced driver convenience and safety:
In
accordance with the hypothesis presented in [
109
], the incorporation of connected car
technologies with big data analytics has the potential to ultimately bring about an
innovative improvement in the services and functions that become available to drivers.
A connected vehicle platform could provide customized services that are targeted
to the driver’s comfort and safety by analyzing large datasets that are derived from
vehicular sensors, user preferences, and other sources of information. These services
are aimed at improving the driver’s experience. This includes things like real-time
traffic information, adaptive ambient settings, predictive maintenance, and automated
emergency response systems.
Towards the final analysis, the collection and management of large amounts of data
have become an essential component in the development of technology related to AI. In
particular, the fields of automated machine learning, clustering, Gibbs sampling, and data
structures [
110
–
113
] have emerged in recent days due to their robustness. Particularly
in managing big data on AVs, there is a vital part in the pipeline by which autonomous
vehicles consume and process a vast amount and variety of data, which is essential for
improving safety, security, efficiency, and the overall user experience. However, the volume
and complexity of big data simultaneously induce a range of security difficulties. These
challenges need ongoing study and improvement in data protection and cyber security
solutions, since they are always evolving. Towards the final analysis, the collection and
analysis of large amounts of data have become an essential component in the development
of the technology behind autonomous vehicles, playing a vital part in the pipeline by which
autonomous vehicles consume and process a vast amount and variety of data, which is
essential for improving safety, security, efficiency, and the overall user experience.
J. Cybersecur. Priv. 2023,3516
8. Blockchain in Autonomous Vehicles
The potential of blockchain technology could revolutionize the operations and func-
tionality of autonomous vehicles. As a distinctive form of distributed ledger technology
(DLT), blockchain provides a platform for secure and transparent transactions and data
exchanges, which are pivotal to autonomous vehicles. It strengthens security, maintains
privacy, and builds trust among users, vehicles, and the various entities embedded in the
transportation ecosystem. Moreover, it facilitates real-time data exchange, enhances the
decision-making abilities of autonomous vehicles, and paves the way for innovative busi-
ness models. With the integration of blockchain technology, a more efficient, interconnected,
and intelligent transportation system can be envisaged.
Blockchain technology can be used in autonomous vehicles in several ways:
1.
Enhanced situation awareness: Blockchain technology offers a secure and reliable
mechanism for autonomous vehicles to share information, elevating their situational
awareness and decision-making capabilities. It enables vehicles to exchange real-time
data regarding traffic conditions, potential road hazards, and other relevant factors.
This collected information is stored in the blockchain, which provides a transparent
and tamper-resistant record [114].
2.
Reputation management: Employing blockchain technology enables the develop-
ment of a reputation system for autonomous vehicles in which reputation points
are awarded for the sharing of accurate and beneficial data. This enables vehicles
to actively contribute to the network and ensures the transmission of high-quality
data [115].
3.
Security for firmware updates: Blockchain can be utilized to secure firmware updates
in autonomous vehicles, mitigating the risk of harmful attacks that could compromise
the vehicle systems [116].
4.
Liability attribution: In scenarios involving accidents with autonomous vehicles,
blockchain technology can play a crucial role in accurately identifying the vehicle at
fault. This assists in resolving disputes and ascertaining fair liability assignment [
117
].
5.
Ride-hailing platforms: Blockchain technology is applicable in the creation of secure,
decentralized ride-hailing platforms for autonomous vehicles. Such platforms provide
a secure and transparent process for users to arrange rides and for vehicles to receive
appropriate compensation for their services [118].
6.
Internet of Vehicles: The implementation of blockchain can facilitate a secure, decen-
tralized network of vehicles or devices, drawing parallels to the Internet of Things.
This would enable vehicles to communicate amongst themselves, share data, and
coordinate actions in a secure, transparent manner [119,120].
8.1. Blockchain Applications
According to the published research in this field, blockchain technology has the
potential to transform a variety of different aspects of autonomous cars. According to
Kamble [
121
] and Show [
122
], utilizing blockchain technology may strengthen security
measures, expedite vehicular operations, and make collaborative storage options more
accessible. Pedrosa and Pau
[123]
focused on the application of blockchain technology in
intelligent transportation settings, with a specific emphasis on the function that blockchain
technology plays in the automation of contracts and transactions for the recharging process
in autonomous electric cars. Furthermore, Jain et al.
[124]
investigated the numerous
applications of blockchain technology in a variety of autonomous vehicles and systems.
These include autonomous electric vehicles (AEVs), autonomous underwater vehicles
(AUVs), automated guided vehicles (AGVs), autonomous aerial electric vehicles (AAeVs),
and autonomous driving systems. In conjunction, the findings of this research highlight
how the use of blockchain technology in the field of autonomous cars may improve
trustworthiness, operational dependability, and system efficiency.
When cyber security is in the spotlight, blockchain is at the forefront of mitigating the
numerous risks associated with autonomous vehicles [
125
]. Securing over-the-air (OTA)
J. Cybersecur. Priv. 2023,3517
software and firmware updates, a crucial aspect of the maintenance and performance of
autonomous vehicles, is a prominent example of blockchain’s capabilities [
126
]. By employ-
ing a blockchain-based framework, the integrity of the update procedure is maintained,
allowing only authentic original equipment manufacturers (OEMs) to distribute software
upgrades and updates. In conjunction with ensuring that only authenticated vehicles can
access and deploy these upgrades, this creates an impenetrable security perimeter around
the OTA procedure [126].
This combination is not without its challenges and potential traps, and it is imperative
that this amalgamation be approached with discernment. The complexities of the data
transmission networks that enable autonomous cars, when combined with the inherent
symmetries of cyber security, have the potential to accidentally pave the path for new
vulnerabilities during data transfers between vehicles and IoT devices [
71
]. This necessitates
a relentless commitment to innovation and the construction of complex countermeasure
models and cutting-edge security algorithms, both of which are specifically geared to
neutralize cyber security vulnerabilities and prevent incidents of data loss [71].
Ultimately, blockchain technology is well-positioned to play an essential role in the
paradigm change that will be brought about by autonomous cars. This will be accomplished
via the implementation of strong security and the guarantee of unrivaled privacy. Although
blockchain is a reliable ally in reducing risks, especially with respect to over-the-air (OTA)
updates, it is essential to remain vigilant and proactive in order to successfully navigate
the problems and vulnerabilities that are brought about by the combination of blockchain
technology with autonomous cars.
8.2. Benefits in AVs
Most research has focused on the crucial role that blockchain technology may play
in the process of transforming autonomous cars. Both the [
119
,
127
] are investigating the
myriad of ways that blockchain technology might enhance cyber security, consequently
producing an impenetrable and reliable infrastructure for the real-time transfer of vehicle
telemetry data. The safety of sensitive vehicle data may be significantly improved by
using the features of blockchain technology that make it inherently secure and impervious
to change.
The discussion is brought to a higher level by Wang’s presentation of a novel framework
known as the blockchain-enabled autonomous vehicular social network (AVSN) [
128
]. This
cutting-edge architecture not only protects information transmission using cryptographic
protocols but also deftly orchestrates an incentive system for connected autonomous
vehicles (CAVs), which encourages them to distribute material that can be verified and
trusted. This helps to maintain the integrity of the material while also building a culture
of cooperation and trust among the many organizations that make up the network. This
brings the ecosystem closer to being in balance.
Alladi et al.
[129]
presented a larger perspective and provided a complete examina-
tion of the several ways in which blockchain technology might be used inside networks
of unmanned aerial vehicles (UAVs). This research study investigated a wide range of
applications, including but not limited to enhancing network security, accelerating decen-
tralized storage paradigms, fine-tuning inventory management, and boosting cutting-edge
surveillance methodologies.
The use of blockchain technology in the field of autonomous vehicles (AVs) has the
potential to usher in a number of significant developments and advantages over a wide
variety of domains. Listed below is an in-depth analysis of these cutting-edge advantages:
•
Blockchain-based collaborative crowd sensing (BCC) in autonomous vehicular net-
works (AVNs): Blockchain prepares the way for an unprecedented, safe environment
that is favorable for intense data transfers and fair recompense. This is an important
step in the development of pioneering vehicular crowd sensing. It does an excellent
job of protecting the privacy of AVs while also assuring the efficient usage of resources
in the process of completing tasks [130].
J. Cybersecur. Priv. 2023,3518
•
Impenetrable security and uncompromised privacy: AVs, which are often afflicted by
intrinsic security weaknesses, may find refuge in the arms of blockchain technology,
which offers impenetrable security and does not compromise privacy. The blockchain
eliminates reliance on any centralized entity, thanks to its decentralized philosophy,
and the immutability of its ledger instills unshakable faith in the system. Its inherent
design principles render it immune to both single points of failure and a wide variety
of security flaws, making it a very secure system. The combination of blockchain
technology and artificial intelligence (AI) creates a sturdy fortress that efficiently
protects autonomous vehicles (AVs) from a wide variety of dangerous threats [131].
•
Uncompromisable data integrity: The combination of blockchain technology and
unmanned aerial vehicles (UAVs) that are incorporated into autonomous vehicles
(AVs) acts as a stronghold for data-related security. This hybrid approach may be
further strengthened by the use of AI, opening the path for an integrated security
fabric that is both robust and adaptable [132].
•
Efficient privacy preservation conventional federated learning (FL) systems, which
utilize direct raw data transfers to servers, are known for the privacy issues that they
invoke. Although blockchain may be a helpful tool in protecting users’ privacy, doing
so comes at the expense of increased computing load. The implementation of gradient
encryption in FL makes it possible to encrypt data in situ. This is made possible by
using the processing power of edge devices. This not only protects the privacy of the
data but also eliminates the need for extra processing resources, and it does all of this
without any additional cost in terms of performance [14].
In a nutshell, the combination of blockchain technology an autonomous vehicle tech-
nology heralds the beginning of a new age of innovation that will be characterized by
groundbreaking vehicular crowd sensing, unshakable security, perfect data integrity, and
resource-efficient privacy conservation solutions.
8.3. Blockchain Potential Risks in AVs
Blockchain technology, heralded as a game changer for numerous industries, also
shows promise in transforming the realm of autonomous vehicles (AVs). However, it is
imperative to carefully navigate the intricate web of challenges and risks associated with
its implementation:
•
Scaling bottlenecks: Major blockchain networks such as Ethereum and Bitcoin ex-
hibit constrained processing capacities, hovering around 5 to 20 transactions per
second [
133
]. When juxtaposed with the high-velocity data exchanges integral to
vehicular networks, these limitations pose significant challenges. It is imperative to
develop or adopt blockchain architectures that are nimble enough to accommodate
the velocity and volume of data inherent in autonomous vehicular systems.
•
Cyber security threats: The complex communication networks that form the backbone
of AV interactions are not impervious to cyber threats. These networks could be
prey to attacks that not only compromise data but also imperil human lives through
accidents instigated by erroneous or manipulated data [
133
]. To address this, cyber
security mechanisms need to be woven into the blockchain fabric to fortify the system
against intrusions and hacks.
•
Navigating privacy concerns: Although blockchain technology’s decentralized ledger
systems improve security, the irreversible nature of data that is held on blockchains
may pose privacy problems if not managed properly. It is essential to make use
of privacy-enhancing technology, either via zero-knowledge proofs or other crypto-
graphic approaches, in order to bring blockchain’s transparency into harmony with
the need for user anonymity and data security.
•
Integration complexities: The rapid rate of innovation in hardware and software stands
in stark contrast to the longer service life cycles of vehicles [
134
]. The integration of
blockchain into the dynamic ecosystem of autonomous vehicles demands an agile ap-
J. Cybersecur. Priv. 2023,3519
proach, possibly through modular and adaptable frameworks that can keep pace with
technological advancements without necessitating wholesale infrastructural changes.
•
Environmental and resource stewardship: Certain blockchain frameworks, particularly
those reliant on proof-of-work consensus algorithms, are notorious for their prodi-
gious energy consumption. Beyond the environmental repercussions, the resource
intensiveness of these systems could also tax the computing capacities within vehicles.
Alternative consensus algorithms such as proof of stake or proof of authority might
mitigate these challenges, striking a balance between security and resource efficiency.
It is crucially important to adopt an intelligent and forward-thinking strategy in order
to maximize the revolutionary potential of blockchain technology in autonomous cars and
enjoy the benefits of this potential. This requires the development and implementation
of blockchain frameworks that are scalable, secure, and resource-efficient, along with the
integration of privacy protections and cyber security defenses. The academic literature
emphasizes both the exciting prospects, as well as the inherently difficult obstacles, that
are associated with the combination of blockchain technology and autonomous cars. The
potential security flaws that are associated with connected autonomous vehicles (CAVs)
were investigated in depth in critical research that was carried out by Rajendar et al. [
135
].
The study focused on the potential solutions that may be provided by blockchain technology.
Notably, the study highlighted the potential of blockchain technology to improve the
reliability and integrity of data transfers, as well as intervehicle communication.
In a study that echoes similar results, Gupta et al. [
136
] conducted an in-depth investi-
gation of the threat scenario posed by CAVs and highlighted the potentially game-changing
role that blockchain technology may play in the fortification of vehicle networks. Their
research went beyond just praising the benefits of blockchain technology and instead pro-
vided concrete insights into how blockchain-based frameworks might be adapted to meet
the specific safety criteria of CAVs.
In addition, Reyna et al. [
137
] investigated the symbiotic relationship between blockchain
technology and the ecosystem of the Internet of Things (IoT). Blockchain emerges as a
formidable tool for maintaining data security, allowing for smooth machine-to-machine
transactions and limiting the single points of failure in centralized systems when connected
autonomous vehicles (CAVs) are used in conjunction with Internet of Things (IoT) networks.
This intersection is of special relevance.
In their comprehensive study of the uses of blockchain technology in intelligent trans-
portation systems, which includes autonomous vehicles (CAVs), Jabbar et al. [
138
] provided
a 360-degree perspective. Their research meticulously catalogs a variety of blockchain
implementations, deconstructing their respective advantages and disadvantages and pro-
viding a comparative analysis of the outcomes. This thorough study provides vital insights
for decision makers and engineers, assisting them in choosing the ideal blockchain architec-
ture that is in harmony with the requirements and restrictions of intelligent transportation.
In conclusion, the academic discussion highlights the enormous potential of blockchain
technology as a key component to improve the safety, dependability, and effectiveness
of CAVs. However, it should also be noted that this is not a problem that can be solved
with a single solution; rather, the deployment of blockchain technology requires careful
evaluation of the obstacles and restrictions it presents. In order to maximize the benefits
that may be derived from the groundbreaking combination of blockchain technology and
autonomous car ecosystems, it is necessary to maintain a high rate of innovation and have
a solid grasp of the complexity involved in both of these fields.
8.4. Blockchain-Based Solutions for AVs
The exploration of blockchain technology application in autonomous vehicles (AVs) is
thoroughly presented across five distinctive aspects in Tables 3–7.
Table 3focuses on the employment of blockchain to enhance the security of data
storage in AVs. The presented solutions are built on Ethereum, Consortium BC, and Private
BC platforms.
J. Cybersecur. Priv. 2023,3520
Table 4highlights the potential of blockchain technology in securing communication
channels, particularly those relating to vehicle-to-vehicle (V2V) networks and in-vehicle net-
works, with works using various platforms, including Ethereum, Bitcoin, and Lightweight
BC.
In Table 5, the attention shifts towards the preservation of data integrity and privacy
in AV systems. The presented solutions address concerns such as AV forensics and location
privacy, employing platforms like Permissioned BC, IoTChain, and IOTA Tangle DLT.
Table 6presents a comprehensive analysis of how blockchain technology can be lever-
aged for forensic purposes in the context of AVs, providing means for accident responsibility
identification and event recording systems.
Table 7expands on research and implementations that focus on utilizing blockchain
for reputation and trust management among AVs. The discussed solutions utilize both the
Public and Consortium BC platforms.
All of the tables below provide valuable insight into the multifaceted role that blockchain
technology can play in addressing challenges in the autonomous vehicle sector. This ranges
from enhancing data security to creating network trust, emphasizing the immense opportu-
nity that blockchain presents for future research and development in this field.
Table 3. Blockchain applications in autonomous vehicles (AVs)—secure data storage.
Description Blockchain Platform Authors Year
Secure cloud-based storage of AV data Ethereum Jiang et al. [139] 2019
Secure data storage and sharing between AVs and RSUs Consortium BC Zhang et al. [140] 2019
Encrypting and hashing the AV data for secure storage Public/Private BCs Singh et al. [141] 2021
Data storage system with incremental AV data updating Ethereum Yin et al. [142] 2021
Secure data storage and sharing among AVs Ethereum Riya et al. [143] 2022
Encrypting and hashing the AV data for secure storage Private BC Meghna et al. [144] 2022
Table 4. Blockchain applications in Autonomous vehicles (AVs)—secure communication channels.
Description Blockchain Platform Authors Year
Securing V2V communications and privacy protection Private BC Singh et al. [145] 2017
Securing V2V communications (V2V network) Ethereum Rowan et al. [146] 2017
Securing the communications between AVs and RSUs Bitcoin Yang et al. [147] 2018
BC-based V2V data aggregation model Hyperledger Fabric Mitra et al. [148] 2018
Securing smart sensors of AVs (in-vehicle network) Ethereum Rathee et al. [149] 2019
Securing in-vehicle network components Private BC Oham et al. [150] 2021
Secure sensing and tracking of AVs Ethereum Dakshita et al. [151] 2021
Secure routing for swarm UAS networking Lightweight BC Wang et al. [152] 2021
BC-based system for secure V2V communication Public BC Kamal et al. [153] 2021
BC-based secure V2V communication using ICN Public BC Ali et al. [154] 2022
J. Cybersecur. Priv. 2023,3521
Table 5. Blockchain applications in autonomous vehicles (AVs)—data integrity and privacy.
Description Blockchain Platform Authors Year
Records all necessary data for an AV forensics solution Permissioned BC Cebe et al. [155] 2018
Protecting the AV identity and location privacy loTChain [156] Li et al. [157] 2018
Ensure safety and information integrity inside the AV Bitcoin Davi et al. [158] 2019
Data integrity by tracking the actions of AVs Exonum platform Narbayeva et al. [127] 2020
Protection against data-tampering attacks in AV network IOTA Tangle DLT Rathore et al. [159] 2020
BC key management framework and hash graphs Permissioned BC Jha et al. [133] 2022
Table 6. Blockchain applications in autonomous vehicles (AVs)—forensics applications.
Description Blockchain Platform Authors Year
Fragmented ledger for forensic analysis of traffic accidents Lightweight BC Cebe et al. [155] 2018
Event recording system for vehicular digital forensics Ethereum Li et al. [160] 2021
BC-based accident responsibility identification model Lightweight BC Yao et al. [161] 2022
BC-based reputation system for AV accident forensics Permissioned BC Oham et al. [150] 2022
Table 7.
Blockchain applications in Autonomous vehicles (AVs)—reputation and trust management.
Description Blockchain Platform Authors Year
Data sharing in V2V using reputation and contract theory Public BC Kang et al. [162] 2019
BC-based solution for reputation management in IoV Ethereum Abbes et al. [163] 2021
Two-layered AV reputation BC system Private/Public BCs Lee et al. [164] 2021
BC-based trust scheme for cellular V2X ecosystems Consortium BC Bhattacharya et al. [165] 2022
BC-based reputation system for secure V2V communications Public BC Kianersi et al. [115] 2022
9. Discussion
9.1. Challenges, Open Issues, and Future Research Directions for EIAVs
The domain of autonomous vehicles (AVs) remains in its growth phase, laden with an
abundance of sensitive information and fresh technical hurdles. The enormous breadth
and complexity of the topic render it multifaceted terrain. This is further compounded by
the current scarcity of comprehensive global standards that guide the development, safety,
and security procedures for AVs. As a result, investigating and addressing issues regarding
the security and safety of AVs is not only highly intricate but also of utmost importance.
As AVs continue to evolve technologically, their ecosystem is expected to broaden to
include a greater number of individual devices and supplementary infrastructure. This
growth is likely to bolster connectivity; however, it could simultaneously render AVs more
vulnerable to a plethora of security risks. This development gives rise to critical questions
and challenges that demand immediate scholarly attention.
•
Protection of V2X communication: Ensuring the security of vehicle-to-everything
(V2X) communication is critical, as a breach in AVs might have cascading effects on
connected smart infrastructure and vice versa. For instance, an attack on an electric
vehicle could ripple through to the electricity grid, charging stations, and utility
systems. Consequently, future research should prioritize the formulation of strong
protective measures and the establishment of secure communication channels.
•
Synchronization of safety and security protocols: Typically, assessments of vehicle
safety and security are performed separately, which leads to the creation of disparate
protective measures. However, fostering a seamless synergy between safety and
J. Cybersecur. Priv. 2023,3522
security protocols within AVs is essential. Therefore, future research must concentrate
on analyzing the inter-relations between safety and security measures to ascertain
their coordinated efficiency.
•
Engagement with non-automated road participants: In mixed traffic conditions,
AVs need to effectively communicate and cooperate with both automated and non-
automated road users, such as traditional vehicles, bicycles, and pedestrians. Gaining
insight into and making predictions about human behavior in mixed traffic situations
is difficult but vital for ensuring safety. There is an urgent need for research that
improves our grasp of the interactions between humans and AVs in these contexts.
•
Safeguarding CAN bus communications: The Controller area network (CAN) bus,
which handles sensor data transmission, is exposed to potential attacks. In the absence
of security measures like encryption, vital mission-planning data can be exposed and
at risk. Future investigations should focus on strategies for effectively safeguarding
CAN bus communications to maintain data integrity and authenticity.
•
Adapting to new attack methods: As technology keeps advancing, so do the methods
used in cyber attacks. Future research needs to stay one step ahead by trying to predict
the new ways that attackers might attempt to breach security and by developing
strategies to prevent these attacks before they can happen.
•
Formulation of holistic standards and regulations: The absence of universal standards
for the safety and security of AVs presents a considerable obstacle. The creation and
enforcement of international norms would supply a uniform framework to steer the
development of secure and reliable AV systems, consequently bolstering the overall
security stance of AVs.
•
Addressing machine learning weaknesses: Machine learning, especially deep learning,
is a cornerstone of AV technology but is vulnerable to targeted attacks. Future stud-
ies should consider the development of solid strategies to shield machine-learning
algorithms in AVs from these potential incursions.
9.2. Challenges
The development of autonomous vehicles signifies a transformative potential for the
future of both private and public transportation systems. Nevertheless, the realization of
this future is contingent upon overcoming numerous obstacles, particularly in the area of
security. As this field of research progresses, it becomes increasingly clear that ensuring the
secure and reliable operation of AVs is a task of considerable complexity. As discussed in
the preceding sections, sensor systems and communication channels, which are integral
to the functioning of AVs, are susceptible to an array of cyber attacks. This necessitates
substantial advances in the areas of image analysis and processing capacity to consistently
and accurately interpret complex driving environments and make optimal decisions in real
time.
In addition to these technical challenges, there are also significant societal, legal, and
ethical hurdles to be explored. The societal acceptance of AVs lies in public confidence
in their safety and reliability, requiring comprehensive and robust safety validation. Fur-
thermore, the regulatory landscape for AVs is still evolving and represents a significant
challenge, with requirements varying across countries. Finally, there are also ethical dilem-
mas to be addressed, relating to the decision-making capabilities of AVs in critical scenarios.
Addressing these multifaceted challenges will be a key part of the journey towards a future
where autonomous vehicles become a widespread reality.
Ultimately, as the world of transportation is experiencing major changes due to the
introduction of autonomous vehicles (AVs), there is a lot of interest in how they could
completely change the way we travel. However, as these vehicles continue to develop,
especially in terms of how they can understand and react to human emotions, there are
many different challenges that emerge. Through our detailed research, we have identified
the following key areas that need attention and investigation.
J. Cybersecur. Priv. 2023,3523
1. Risk mitigation technologies:
Transitioning from manual to automated driving re-
quires a comprehensive understanding of risk mitigation technologies. In the discus-
sion on risk mitigation requirements and problems, the parameters of object detection,
cyber security, and privacy in V2X interactions stand out as significant topics [125].
2. Real-time decision making:
In order for an autonomous vehicle to be considered
competent, it must be able to successfully carry out real-time decision making, which
allows it to outperform the skills of a human driver. Technology must continue
to advance, especially in the areas of high-speed processing and decision-making
algorithms, in order for such achievements to be possible [125].
3. Garnering public trust:
The level of public trust is a crucial factor that determines the
final level of success and broad adoption of autonomous vehicles. This necessitates
an amount of technical accuracy that has never been seen before, establishing faith
in the autonomous vehicle’s capacity to find solutions to problems and ensuring its
overall safety [14].
4. Precision positioning technologies:
The development of technologies that are able
to accurately identify the locations of vehicles is necessary for the production of
intelligent transportation systems that are both safe and dependable. These systems
need to take into account a variety of unknown factors, including the unexpected
behaviors of pedestrians, random objects, and different road conditions [14].
5. Environmental detection:
The ability of an autonomous vehicle to accurately identify
its surroundings is essential to the vehicle’s ability to navigate successfully. As a result,
AV safety relies heavily on the development of technologies that can detect and react
appropriately to a variety of settings [14].
6. Pedestrian detection:
The protection of pedestrians must be given top priority in the
design of autonomous vehicles, which calls for detecting systems that are accurate
and dependable [14].
7. Path planning:
The capacity of an autonomous vehicle to plot its own route is a critical
factor in determining not just its level of safety but also its level of efficiency. As a
result, the creation of technologies that make precise route planning and prediction
possible is of critical importance [14].
8. Motion control:
The successful control of motion is necessary for the safe navigation
of AVs. The development of technologies that can precisely regulate the motion of the
vehicle, even under unanticipated conditions, is an issue of the highest priority [14].
9. Vehicular communication technologies:
The field of V2X communications requires
the development of reliable vehicular communication technologies, which can enable
autonomous cars to connect without any issues with other vehicles and infrastruc-
tures [9].
10.
Traffic management:
The increasing number of autonomous vehicles may make
traffic congestion worse if it is not controlled in an effective and reliable manner.
Innovative methods like policy-based deep reinforcement learning and intelligent
routing are able to optimize traffic flow management, which, in turn, helps mitigate
congestion [166].
Despite the fact that these challenges appear impossible to overcome, they highlight
the considerable future potential for research and innovation in the entire spectrum of
autonomous vehicles.
By addressing these difficulties head-on, we can ensure the integration of autonomous
vehicles into our transportation networks in a way that is both safe and efficient, leading
to a new age of mobility. Researchers can ensure the safe and effective integration of
autonomous cars into our transportation networks by continuing to tackle these challenges
head-on, ushering in a new era of transportation.
Challenges and Proposed Solutions for Autonomous Vehicle Security
Before delving into the specific challenges and their corresponding solutions, it is
important to acknowledge the complex and dynamic nature of security in the autonomous
J. Cybersecur. Priv. 2023,3524
vehicle landscape. The fusion of advanced technologies such as AI, IoT, and blockchain,
while enabling unprecedented levels of automation, also introduces complex security
dimensions that demand comprehensive and innovative solutions. These complexities
are further compounded by the fact that autonomous vehicles operate in real-time and
ever-changing environments, necessitating agile and robust security systems. Therefore,
understanding the potential difficulties in implementing fundamental security concepts
and supplementary features is of paramount importance, as is the identification of prac-
tical solutions to these challenges. Let us explore some of these challenges and their
proposed solutions.
Challenge 1—System complexity:
The complex network of sensors, advanced algo-
rithms, and interconnected systems inherent in autonomous vehicles introduces substantial
complexity to the application of security concepts.
Proposed solution:
Incorporating a ’security by design’ approach, where security
measures are fundamentally integrated at the design inception of the system, is critical.
Additionally, system compartmentalization could limit potential security breaches by
ensuring each component operates independently and is isolated from the others.
Challenge 2—Real-time operation requirements:
Given the real-time nature of au-
tonomous vehicles’ operational decisions, the introduction of latency by certain security
operations, particularly complex encryption algorithms, can impede smooth functionality.
Proposed solution:
The application of lightweight encryption algorithms and hardware-
accelerated security operations can help fulfill real-time requirements without compromis-
ing security.
Challenge 3—Scalability issues:
The exponential growth of data volume exchanged
and managed as autonomous vehicles proliferate can strain the system, thereby complicat-
ing security maintenance and incident response.
Proposed solution:
The implementation of scalable security solutions is essential.
Here, distributed ledger technologies such as blockchain can ensure security and privacy
in a decentralized and scalable manner.
Challenge 4—Long vehicle lifespan:
The typical life cycle of vehicles far outlasts the
rapid evolution of cyber threats, posing a unique challenge.
Proposed solution:
Over-the-air (OTA) updates can be instrumental in enabling
vehicles to continually update their security systems to counter new threats. Ensuring
backward compatibility of these updates is a critical aspect of this solution.
Challenge 5—legislation and standards:
The accelerated pace of autonomous vehicle
technology development often surpasses the existing legislation and security standards.
Proposed solution:
A cooperative effort involving vehicle manufacturers, cyber secu-
rity experts, and policymakers is necessary to establish and regularly update regulations
and standards specifically tailored to autonomous vehicles.
Although these challenges are significant, they are not unsolvable as long as there is
continual research, innovation, and a concerted effort to collaborate across the numerous in-
dustries that are contributing to the development and deployment of autonomous vehicles.
9.3. Open Topics
In order to acquire a more complete understanding of the immense potential and
inherent complexities involved in the development of emotionally intelligent autonomous
vehicles (EIAVs), the following clarifications may be considered:
•In-vehicle health monitoring [167]:
An enhanced focus on health monitoring within
vehicles could lead to the incorporation of sophisticated biometric and physiological
sensors. These could track passenger vital signs, stress levels, and emotional states.
EIAVs, equipped with advanced health monitoring systems could adapt their behavior
in real time to ensure a safer and more comfortable journey. For example, detecting
elevated stress levels could trigger a more conservative driving style or initiate a
calming ambient environment.
J. Cybersecur. Priv. 2023,3525
•Simulation-based testing and validation [168]:
Enriching simulation-based testing
for emotionally intelligent autonomous vehicles (EIAVs) necessitates the introduc-
tion of multifaceted emotional scenarios. This incorporation brings a novel layer of
complexity to the validation process. For instance, the manner in which an EIAV
responds to a passenger experiencing emotional distress becomes a crucial metric of
its performance. Similarly, the vehicle’s ability to identify and respond appropriately
to a passenger’s discomfort in heavy traffic or concern about speed forms an essential
part of its evaluation. The development of such emotional scenario databases for
rigorous testing, followed by a thorough assessment of EIAVs’ responses, constitutes
a pivotal aspect of their evolution.
•Underwater Internet of Things (UIoT) [169]:
Gaining higher-level abstractions or
insights from the UIoT, EIAVs could incorporate underwater vehicular communication
protocols for specific applications, such as underwater rescue or exploration vehicles.
The unique challenges and solutions in UIoT communication could provide valuable
lessons to enhance V2X (vehicle to everything) communication in EIAVs, even under
challenging conditions.
•Intelligent data processing methods [170]:
To enhance EIAV capabilities, we might
integrate artificial intelligence and machine learning techniques to analyze the vast
amount of data these vehicles would generate and receive. Emotional data, in particu-
lar, can be complex and multimodal, necessitating sophisticated, AI-driven approaches
for reliable interpretation and reaction.
•Autonomous traffic management (ATM) [171]:
Extending the scope of ATM to in-
clude affective factors is an intriguing area of research. For instance, traffic congestion,
an external factor, invariably impacts the overall mood of a passenger within a vehicle.
An advanced EIAV could be designed to intelligently respond to such circumstances.
It may proactively select routes less predisposed to causing passenger stress or lever-
age onboard systems to sustain a tranquil environment, irrespective of the traffic
conditions outside. This indicates that future EIAVs must not only be able to navigate
efficiently through tangible road networks but also to understand the complex nature
of human emotions.
These topics further underscore the groundbreaking potential of EIAVs and the mul-
titude of open-ended research avenues they present. By employing a cross-disciplinary
approach and incorporating lessons learned from related disciplines, the emergence of
EIAVs as an essential component of our transportation ecosystem becomes more plausible.
9.4. Cyber Security Risks and Safety Concerns of EIAVs
Emotionally intelligent autonomous vehicles (EIAVs) represent an innovative integra-
tion of artificial intelligence and machine learning. These vehicles are designed with the
capability to understand and adapt to human emotions, thereby promising a transformative
shift in the transportation sector. However, alongside the innovative possibilities, they also
introduce considerable cyber security risks and safety challenges, which necessitate com-
prehensive research and strategic intervention. Several crucial points have been identified
for the successful deployment of EIAVs:
Cyber security threats:
•
Sensor and communication security: EIAVs rely heavily on components such as
camera sensors, global positioning systems (GPS), and V2X (vehicle-to-everything)
communication protocols. These are potential targets for cyber attacks. Therefore,
robust protective measures can be instituted to secure these crucial components, as
presented in [91].
•
Onboard unit (OBU) security: The OBU functions as the central processing unit of
an autonomous vehicle and can be subjected to cyber exploitations, facing significant
threats to the vehicle’s safety and functionality. Strengthening the cyber security
defenses of the OBU is an essential aspect of protecting EIAVs, as highlighted in [91].
J. Cybersecur. Priv. 2023,3526
•
Systemic vulnerabilities: The systemic complexity that is present in EIAVs expands
the potential surfaces for cyber attacks. Comprehensive cyber security strategies
are essential to ensure that all points of potential intrusion are secured, as described
in [172].
•
Interconnected system vulnerabilities: The interconnected and interoperative nature of
systems within EIAVs can introduce unintended security gaps. Early identification and
rectification of these vulnerabilities are critical to a robust and secure approach [172].
•
Proprietary system risks: Proprietary systems or integrated systems that lack op-
timal interaction with other systems could present additional security challenges.
The development and implementation of standardized protocols ensuring seamless
interoperability are necessary to alleviate these risks [173].
•
Edge computing concerns: The incorporation of edge computing and localized nodes
in EIAVs can lead to severe privacy and security issues. Addressing these requires a
thorough evaluation and subsequent enhancement of existing security protocols, as
mentioned in [174].
Safety concerns:
•
Risk mitigation technologies: To maintain safety standards, concurrent advancements
in risk mitigation technologies are indispensable in the progression of autonomous
vehicles [125].
•
Object detection and V2X privacy: The establishment of rigorous safety standards for
object detection and V2X privacy is integral to the reliable operation of EIAVs. The
formulation of these norms remains a significant area of research [125].
•
Evaluation of autonomous technology: A comprehensive analysis of the potential
benefits and risks of autonomous technology is required for its effective integration
into conventional transport systems. This objective evaluation must be coupled with
robust risk mitigation strategies [125].
•
Intelligent navigation capabilities: The ability of EIAVs to intelligently interact with
and navigate safely among other road users is fundamental to their operational
viability. Enhancing these capabilities forms a crucial part of improving the overall
safety of EIAVs [125].
9.4.1. Consequences for People’s Lives and the Economy
Autonomous vehicles are becoming increasingly popular, and with their rise comes the
need for robust security measures to protect against potential cyber attacks. Security flaws
in autonomous vehicles can have significant impacts on people’s lives and the economy.
Listed below are some ways in which security vulnerabilities in autonomous vehicles could
impact people’s lives and the economy:
•
Safety: Security flaws in autonomous vehicles can compromise the safety of passengers
and other road users. For example, a hacker could take control of an autonomous
vehicle and cause it to crash or drive recklessly [175].
•
Privacy: Autonomous vehicles collect a lot of data about their passengers, such as
their location and driving habits. If these data fall into the wrong hands, they could be
used for nefarious purposes [175].
•
Economy: Autonomous vehicles have the potential to revolutionize the transportation
industry, but security flaws could slow down their adoption. If people do not trust
autonomous vehicles to be secure, they may be less likely to use them, which could
have a negative impact on the economy [175,176].
It is important to note that security flaws in autonomous vehicles are not just theoretical.
Researchers have already demonstrated that wireless jamming attacks can impact the fuel
efficiency of cooperative adaptive cruise control (CACC) systems [
177
]. Additionally, there
is a lack of effective infrastructure for evaluating security solutions for autonomous vehicles.
This means that there is still a lot of work to be done to ensure that autonomous vehicles
are secure [178].
J. Cybersecur. Priv. 2023,3527
In conclusion, security is a crucial aspect of autonomous vehicles, and the potential
repercussions of a breach are significant. It is important to continue researching and
developing security measures to protect against potential cyber attacks.
9.4.2. Fundamental Security Principles
Fundamental security principles are crucial for protecting the security of autonomous
vehicles and the dangers they seek to counteract. Autonomous vehicles rely on complex
software and hardware systems to operate [
179
], and any vulnerabilities in these systems
can be exploited by attackers to gain control of the vehicle or cause it to malfunction. This
can result in serious safety risks for passengers, other drivers, and pedestrians.
Some of the key security principles that are important for protecting autonomous
vehicles include:
•
Safety and Data protection: Autonomous vehicles generate and process vast amounts
of data, including sensor readings, location information, and communication data.
Protecting these data from unauthorized access or manipulation is essential to main-
tain privacy and prevent misuse. Ensuring the security of these systems is vital to
prevent unauthorized access or malicious attacks that could compromise the safety of
the vehicle and its occupants [179].
•
System integrity: Autonomous vehicles rely on complex software and hardware
systems to operate effectively. By adhering to security principles such as secure coding
practices and regular vulnerability assessments, the integrity of these systems can be
maintained, reducing the risk of system failures or malfunctions [180].
•
Resilience to attacks: Autonomous vehicles are potential targets for cyber attacks,
which can range from unauthorized access to the vehicle’s systems to remote control
of its functions. Implementing security principles helps to identify vulnerabilities,
establish robust defenses, and develop incident response plans to mitigate the impact
of attacks and ensure the vehicle’s continued operation [180].
•
Public trust: Security principles are essential for building and maintaining public trust
in autonomous vehicles. By implementing robust security measures, autonomous
vehicle manufacturers and operators demonstrate their commitment to protecting the
safety and privacy of users [179].
•
Perception security: Autonomous vehicles heavily rely on perception, such as obstacle
detection, traffic sign detection, lane detection, etc. With the power of deep learning
algorithms, such perception tasks in autonomous driving systems widely apply deep
neural network (DNN)-based models. Recent works have found that DNN models are
generally vulnerable to adversarial examples or adversarial attacks. Thus, studying
the security of perception in autonomous driving systems under physical-world
adversarial attacks is very necessary [181].
•
Data poisoning attack: The development of connected and autonomous vehicles
(CAVs) relies heavily on deep learning technology, which has been widely applied to
perform a variety of tasks in CAVs. On the other hand, deep learning faces some secu-
rity concerns. Data poisoning attacks, as one of the security threats, can compromise
deep learning models by injecting poisoned training samples. Therefore, the principles
of poisoning attacks are worth studying in order to propose countermeasures [182].
•
Intrusion detection: Intelligent transportation systems (ITSs), particularly autonomous
vehicles (AVs), are susceptible to safety and security concerns that impact in people’s
lives. The safekeeping of communications and computing constituents of AVs can
be threatened using sophisticated hacking techniques, consequently disrupting AVs
from operative usage in our daily life routines. In this regard, a multistage intrusion
detection framework can be used to identify intrusions from ITSs and produce a low
rate of false alarms. The proposed framework can automatically distinguish intrusions
in real time [183].
These principles are important for protecting the security of autonomous vehicles and
the dangers they seek to counteract. Autonomous vehicles rely on complex software and
J. Cybersecur. Priv. 2023,3528
hardware systems to operate, and any vulnerabilities in these systems can be exploited
by attackers to gain control of the vehicle or cause it to malfunction. By following these
fundamental security principles, autonomous vehicle manufacturers can help to protect
the security of their vehicles and prevent them from being exploited by attackers.
9.5. Privacy Preservation Techniques in Vehicular Communications
Considering the increasing popularity of vehicular networks, privacy preservation
emerges as a vital concern, specifically in transactions between autonomous vehicles
and third-party entities, such as traffic management systems. The following methods
underscore the key techniques employed to uphold the privacy of information within these
interactions:
Encryption:
As a foundational method in information security, encryption transforms
data into a code to impede unauthorized access. Various algorithms—both symmetric (e.g.,
AES and DES) and asymmetric (e.g., RSA and ECC)—are employed to safeguard sensitive
data [184].
Secure communication protocols:
During data transit, the adoption of secure com-
munication protocols is integral. Protocols such as transport layer security (TLS) and
Internet protocol security (IPSec) deploy robust encryption and authentication mechanisms
to maintain data confidentiality and integrity [185,186].
Anonymization:
To protect the identity of individuals or vehicles, anonymization
techniques are used. These involve the removal or encryption of personally identifiable in-
formation (PII) such as location, image, and license number [
187
]. For instance, pseudonym
systems can be employed where vehicles are allocated pseudonyms, obscuring the trace-
ability of the vehicles’ real identity.
Blockchain technology and blockchain smart contract code:
The decentralized na-
ture of blockchain technology fortifies data privacy within vehicular networks. It ensures
that every data transaction is recorded and authenticated transparently and securely, pro-
hibiting unauthorized alteration or access. In [
188
], the authors reviewed the applied
state-of-the-art formal methods of smart contract specification and verification with the
aim of reducing the risk of faults and bug occurrence and avoiding possible resulting costs.
However, most approaches fail to reflect the characteristics of the blockchain and user
behavior. In, [
189
], the authors proposed a novel formal modeling approach to verify the
execution environment behavior of smart contracts. The authors applied this formalization
to a real-world example of a smart contract and analyzed its violations using a statistical
model verification technique [189].
Differential Privacy:
This methodology allows the public dissemination of informa-
tion about patterns within a dataset, while safeguarding the individual data points within.
In the context of vehicular networks, differential privacy can be leveraged when sharing
data with third parties, thereby preventing any possibility of reverse engineering to single
out individual vehicles or users.
Homomorphic encryption:
This method allows for computations to be conducted
on encrypted data without the necessity of decryption. As such, a third-party system
could analyze and work with the received data while preserving the privacy of the raw
data [190,191].
Federated learning for autonomous vehicle privacy protection:
Federated learning
is a distributed machine learning technique that allows models to be trained collaboratively
without directly sharing the data [
192
]. Instead of transmitting individual client data to a
central server, the central server sends its model to the clients, and each client trains the
model with its own data. This approach helps protect the privacy of the data collected by
autonomous vehicles while still allowing for model improvement [187,193].
Personalized kanonymity:
Personalized
k
anonymity is a privacy preservation tech-
nique that ensures that query contents submitted by users in autonomous vehicles are
sufficiently protected. It achieves this by perturbing location information or applying
k
-
J. Cybersecur. Priv. 2023,3529
anonymity techniques, which group queries together to provide privacy while maintaining
query utility [194].
As part of their functioning, autonomous vehicles need to communicate with various
external entities, such as other vehicles (V2V communication), infrastructure (V2I com-
munication), and broader networks (V2N communication). This communication process
often involves the transmission of sensitive data, including vehicle location, speed, naviga-
tion details, and even potentially sensitive user information. Therefore, ensuring privacy
becomes crucial.
Each method described in this subsection—encryption, secure communication pro-
tocols, anonymization, blockchain technology, differential privacy, and homomorphic
encryption—serves to protect the privacy of data communicated between autonomous
vehicles and third-party entities.
Ultimately, while these techniques are not exclusive to autonomous vehicles and can
be applied to various domains where secure data transmission is required, they are highly
relevant and necessary in the context of autonomous vehicular communications.
9.6. Future Research Directions
The future of cyber security for emotionally intelligent autonomous vehicles (EIAVs)
calls for a multidisciplinary approach that embraces advancements in a broad spectrum
of technological domains. One such promising proposition arose from Gupta [
136
], who
suggested the incorporation of blockchain technology to reinforce security measures within
EIAVs. The unique attributes of blockchain, notably its decentralization and immutability
characteristics, have been widely recognized for their potency in securing digital trans-
actions against unauthorized intrusions and manipulations. The decentralized nature
of blockchain can act as a potent defense mechanism, distributing data across multiple
nodes and thereby significantly reducing the risk of a centralized attack. This approach can
effectively prevent single-point-of-failure attacks, providing an extensive and robust type
of defense for EIAVs. Furthermore, the built-in integrity of blockchain technology creates
an incorruptible digital database of transactions that can be programmed to record virtually
anything of value, including sensitive vehicular data. This feature not only offers an added
layer of data security but also promotes transparency and traceability, thus fostering trust
in the EIAV ecosystem.
However, the application of blockchain technology in EIAVs is still in its first steps and
presents its own set of challenges and research opportunities. The integration of blockchain
with EIAV systems requires extensive exploration to ascertain the optimal approaches for its
deployment. This includes but is not limited to determining the type of blockchain (public
or private), the consensus protocol, the handling of scalability issues, and the management
of privacy concerns. In addition, the unique characteristics of EIAVs, such as the real-
time requirement and large-scale data generation, pose new demands for blockchain
technology, which calls for further innovations and improvements. Therefore, Gupta’s
proposal illuminates a pivotal research direction that requires extensive technological
exploration, rigorous testing, and continuous optimization to fully harness the potential
of blockchain technology to bolster EIAV security [
136
]. The adoption of proactive cyber
security measures, a concept ably advanced in [
27
], is a necessary complement to this effort.
Prioritizing real-time threat detection and responsive algorithms holds great promise to
prevent cyber security threats and eliminate them before they develop into severe attacks.
Future EIAV research endeavors must resolutely address this aspect, nurturing proactive
and preventative cyber security measures.
In [
183
], an enhanced multistage intrusion detection framework was proposed for
autonomous vehicles (AVs) and intelligent transportation systems (ITSs). Recognizing
that these systems are susceptible to sophisticated hacking techniques, the researchers
introduced a bidirectional long short-term memory (LSTM) architecture that efficiently
identifies intrusions in real time. Their integration of a normal state-based mechanism
along with deep learning techniques suggests an effective method for managing complex
J. Cybersecur. Priv. 2023,3530
attack scenarios. This direction, as demonstrated effectively through extensive testing,
accentuates the potential of deep learning techniques in augmenting cyber security in AVs
and ITSs. He et al.
[180]
proposed a comprehensive machine-learning-based detection
framework for connected and autonomous vehicles (CAVs) to prevent cyber attacks. They
proposed a UML (unified modelling language)-based cyber security framework founded on
the CAV cyber security principles of the United Kingdom. Their distinctive contribution is
the development of a novel CAV communication cyber-attack dataset (CAV-KDD), which is
tailored to cyber attacks based on communication. Their findings highlight the importance
of machine learning, supported by structured cyber security frameworks, in proactively
protecting CAVs against potential cyber threats.
Torre et al.
[195]
emphasized the importance of securing the integral vehicular technolo-
gies of emotionally intelligent autonomous vehicles (EIAVs), including sensing, positioning,
and vision systems. As these systems serve as the foundation on which EIAVs operate
and interact with their environment, they become prime targets for potential cyber attacks.
Consequently, the development of system-specific security methodologies that provide a
comprehensive defense is a crucial area for future research. This would entail analyzing the
unique security requirements of each component, determining its inherent vulnerabilities,
and customizing defensive strategies that are not only reactive but also anticipatory of
potential cyber-attack patterns. In addition, these security methodologies would need
to be adaptable and scalable to incorporate new technologies and standards, given the
accelerated rate of technological advancement. Therefore, future research must incorporate
the multifaceted task of designing, validating, and deploying comprehensive security
methodologies that can protect these vital vehicular technologies from potential cyber
attacks.
The findings of Amara et al.
[24]
underscore the critical need to comprehend potential
attack vectors aimed at autonomous vehicle software and hardware. Due to the intricacy
and interconnectedness of these systems, they offer cyber attackers numerous access points
of entry. A thorough comprehension of these threats requires a detailed mapping of these
potential attack vectors, as well as the identification of their patterns and implications.
This would then inform the design of mitigation strategies that effectively mitigate these
vulnerabilities. In addition, this comprehension must encompass the consequences of
effective cyber attacks, ranging from immediate operational disruptions to long-term effects
on user confidence and regulatory compliance. Future research should therefore employ a
twofold strategy: constructing a detailed threat landscape particular to autonomous vehicle
software and hardware and developing holistic countermeasures that can neutralize threats
while ensuring optimal performance. This essential research direction would considerably
contribute to the fortification of EIAV systems, thereby facilitating their secure incorporation
into our social fabric.
The highly sensitive emotional data involved in EIAVs pose a complex challenge
that deserves particular attention. Identifying and implementing optimal strategies to
protect these data from potential cyber threats will be crucial. This protective layer may
encompass encryption techniques, privacy-preserving computation, and anonymization
methods designed exclusively for emotional data. Lastly, considering the novel nature of
EIAVs, the development of regulatory frameworks that effectively address their unique
cyber security needs without impeding their technological advancement is critical. This
endeavor will require a cooperative approach involving researchers, industry stakehold-
ers, and policymakers. Moreover, some additional and potential research directions are
presented below:
1. TinyML in autonomous vehicles for cyber security enhancement and predictive
defense:
TinyML appears as a crucial tool in the world of autonomous vehicles (AVs),
as first presented in [
196
], that has the potential to radically alter the structure of the
security apparatus. Autonomous cars are often outfitted with a variety of sensors
and communication modules, both of which are constantly producing new data. Pro-
cessing these data centrally or in the cloud might be resource-intensive and, more
J. Cybersecur. Priv. 2023,3531
importantly, offer a broader attack surface to prospective cyber enemies. The applica-
tion and capabilities of TinyML may be considered in this aspect. TinyML allows for
the localized processing of data by applying lightweight machine learning algorithms
directly inside the embedded systems of the vehicle [
196
]. This ultimately results in
a reduction in latency and a significant reduction in the possibility of data breaches
occurring during transmission. Additionally, TinyML can be designed to continually
monitor the sensor data and network activity inside the car for any anomalies [
197
].
TinyML has the ability to learn to recognize potentially suspicious patterns that may
indicate a cyber assault. These patterns may include an effort to modify sensor read-
ings or control messages. TinyML can learn to recognize these patterns via the use
of powerful machine learning models. When an anomaly is discovered, it is possible
for it to immediately trigger countermeasures. These countermeasures may include
disconnecting the compromised component or contacting a central security system.
In addition, since TinyML utilizes such a little amount of power, it is able to maintain
its operational state even when the vehicle is not in use, which ensures that the user is
always protected. Additionally, the predictive capabilities of TinyML models could be
applied to anticipate developing attack vectors, allowing for the creation of proactive
security measures. This can be accomplished via the utilization of TinyML’s predictive
analytics. TinyML’s incorporation into autonomous vehicles provides, in essence,
an effective method for enhancing cyber security, shortening the reaction times to
security events, and ensuring the integrity and resilience of vehicular systems in the
face of an ever-evolving environment of cyber threats.
2. Integration of reinforcement learning, Markov decision processes, and intelligent
Rainbow DQN agents in AV cyber security: The combined power of reinforcement
learning (RL), Markov decision processes (MDPs), and intelligent Rainbow DQN
agents can serve as a formidable arsenal to enhance the cyber security of autonomous
vehicles (AVs). Reinforcement learning, another type of machine learning, can be
applied to analyze the patterns and potential vulnerabilities in AV systems by con-
tinuously learning from the environment and optimizing responses under different
circumstances. Within the framework of Markov decision processes, RL algorithms
can be implemented. MDPs evaluate the present state of the system, available actions,
transition probabilities, and rewards, enabling RL algorithms to make decisions that
maximize some concept of cumulative reward. This is particularly beneficial in the
context of cyber security, as the system can learn to make judgments that reduce
or prevent incidents related to cyber security. At this point, Rainbow DQN agents
incorporate a number of developments in deep Q networks (DQNs) and reinforce-
ment learning. These agents are proficient at managing high-dimensional state spaces,
which are typical of AV systems with numerous sensors and intricate networking.
This knowledge can be efficiently processed by Rainbow DQN agents, enabling them
to make rapid choices based on knowledge. Regarding cyber security, these intelligent
agents may be deployed to continuously monitor the in-vehicle networks and data
flows. They can detect anomalous patterns or intrusions that may be indicative of cy-
ber attacks or system compromises by gaining knowledge from the data. In addition,
through reinforcement learning and its decision-making process, these agents have
the ability to predict the likely evolution of an attack, enabling preventative steps to
be taken before the attack can have a negative impact. As an example, if an attack
pattern is identified, the agent can decide to isolate portions of the vehicle’s network,
restrict communication with the suspected compromised components, or activate
other defense mechanisms. This proactive and learning-based approach facilitated
by the synergy of RL, MDPs, and Rainbow DQN agents can substantially increase
the resilience and security of autonomous vehicles against complicated and evolving
cyber threats.
In conclusion, these outlined future research directions underscore the need for a
holistic, comprehensive approach to cyber security within AVs. Achieving a secure future
J. Cybersecur. Priv. 2023,3532
for emotionally intelligent autonomous vehicles necessitates a concerted effort across
multiple disciplines, including cyber security, emotional recognition, AI, and autonomous
vehicle development.
9.7. Blockchain Technologies in Autonomous Vehicles: Potential Future Solutions
The potential integration of blockchain technology with autonomous cars is a game-
changing step forward for the field of automotive engineering. Blockchain technology,
which is a distributed ledger system that has gained recognition for its robust security
methods and decentralized design, can revolutionize many aspects of how autonomous
cars function. The built-in characteristics of blockchain can be used to improve data secu-
rity and integrity in a wide variety of ways. Additionally, the cryptographic foundations
and consensus algorithms of blockchain may strengthen vehicular communication net-
works, guaranteeing tamper-resistant data transmission and protecting against harmful
intrusions. Integrating blockchain in an era when data are a precious commodity safe-
guards data integrity and authenticity, establishing trust and dependability in autonomous
vehicular ecosystems.
The incomparable capacity of blockchain technology to maintain a digital record of a
vehicle’s history is a noteworthy application that necessitates special consideration. For
instance, blockchain can be utilized to securely record a vehicle’s damage history if it is
deployed with efficacy. This generates a digital document that exhaustively stores every
instance of damage, along with the corresponding restorations and associated information.
Utilizing this information in the persistent registry of a blockchain paves the way for
a significant reduction in fraud and generates an unprecedented level of transparency
and assurance for a diverse group of stakeholders, including vehicle owners, insurance
companies, and prospective buyers of used cars. Moreover, it is conceivable that blockchain
technology could be utilized effectively to serve as a foundation of truthfulness in the
documentation of vehicle mileage. With blockchain’s inherently secure structure, an
immutable record of a vehicle’s mileage can be maintained, effectively identifying odometer
modifications. This innovation serves as a sentinel, ensuring an accurate depiction of a
vehicle’s utilization, thereby imparting a greater degree of transparency and nurturing
confidence in transactions related to the vehicle.
When it comes to autonomous vehicles (AVs), blockchain technology opens up an
even wider range of possibilities. By integrating blockchain, automobile manufacturers can
create a complete and impenetrable library of each vehicle’s service history. This record,
which is stored on the blockchain, is immune to malicious revisions by unauthorized third
parties and can only be updated by organizations with the necessary authority, such as car
manufacturers or licensed service providers. This secure service history not only increases
the intrinsic worth of the car but also advocates for the vehicle owner’s cause by protecting
their rights and establishing a culture of precise maintenance standards.
Furthermore, blockchain technology appears as a cutting-edge alternative for greatly
streamlining and optimizing the settlement procedure for disputes arising from automobile
accidents. In the unfortunate event of an accident involving autonomous cars, blockchain
acts as a channel for a fast, impermeable, and transparent data exchange amongst the
parties involved. This quick and secure exchange of essential data may speed up insurance
claim adjudication procedures, bring disagreements to a solution, and protect car owners’
privacy and security. Furthermore, these data may be used to provide insights into accident
causation, assisting in the development of improved safety features and practices. In
addition, the use of blockchain technology in emotionally intelligent autonomous vehicles
(EIAVs) appears as an especially promising area. Blockchain’s strict security features and
confidentiality promises may be critical in securing the delicate emotional data that EIAVs
capture. The preservation of these data is crucial not just for the users’ privacy but also for
the dependable operation of EIAVs, since they rely on emotional data for successful decision
making. By using the blockchain’s decentralized and irreversible qualities, the integrity
and confidentiality of emotional data can be maintained, supporting progress in the field
J. Cybersecur. Priv. 2023,3533
of EIAVs while adhering to ethical norms and respecting individual privacy. A variety
of advantages incorporating blockchain technology in autonomous cars are illustrated in
Figure 6.
Ultimately, the adoption of blockchain technology has the potential to transform the
security environment for autonomous cars by building a tiered security architecture that
maintains data integrity, promotes transparency, and strengthens privacy guidelines. The
use of blockchain is especially important in the case of emotionally intelligent autonomous
vehicles, given the delicate nature of the emotional data being handled. Therefore, the need
for strong data security measures becomes even more essential. Blockchain technologies
provide an intuitive and effective solution to these needs, making its adoption not just a
choice but a necessity for the responsible and ethical growth of EIAVs. This leads to a col-
laborative effort from stakeholders across the spectrum, including academics, technologists,
and politicians, to foster an environment favorable to the smooth integration of blockchain
technology in the rapidly evolving area of autonomous cars.
Immutable Ledger Comprehensive Recordkeeping
Damage History Documentation
Benefits Vehicular Mileage Tracking
Enhanced Transparency
Reduction in Fraud
Secure Service History
Authorized Access
Secure Data Exchange
Privacy Protection
Security of Sensitive Information
Cryptographic Security
Efficient Information Sharing
Figure 6. Benefits and potential future solutions of AVs.
10. Conclusions
This article explores the ecosystem of autonomous vehicle technology, a field that is
swiftly advancing and reshaping the transportation landscape due to recent innovations.
Despite the immense potential of these innovations, their widespread adoption faces
major difficulties. The dual concerns of cyber security and safety stand out as pivotal
elements that necessitate a thorough investigation and proactive countermeasures. Our
investigation demonstrates both the fundamental principles underpinning AV technology
and the complicated information security dynamics behind their secure operation. We
provided a comprehensive overview of potential cyber security attacks, with a particular
emphasis on the emerging and unmitigated threat posed by machine learning (ML) attacks
on deep neural networks (DNNs).
The major threat presented under various cyber threats is a growing field of au-
tonomous vehicle (AV) research that has attracted considerable academic interest and
scholarly research. The lack of effective countermeasures to machine learning (ML) attacks
on deep neural networks (DNNs) is a critical deficiency in our defensive mechanisms
against the ever-evolving cyber security threats aimed at AVs. Unquestionably, the re-
silience and impregnability of AVs against such intrusions will be crucial in determining
their future growth and gaining wider societal approval. Identifying, categorizing, and
comprehending these prospective assaults is an additional crucial aspect of our work.
We have classified these threats based on the principles of data availability, authenticity,
integrity, and confidentiality, providing a comprehensive analysis of the threats currently
confronting the AV industry. In addition to demonstrating the current status of the threat
J. Cybersecur. Priv. 2023,3534
landscape, this classification uncovers the gaps in existing defense strategies, thereby spot-
lighting crucial research and mitigation areas. For each type of attack, we determined
its status in terms of mitigation: fully mitigated for threats that have been completely
neutralized by existing countermeasures, partially mitigated for threats that remain viable
under certain circumstances, and uncovered for threats that require additional research
or for which existing solutions have proven insufficient. The aforementioned findings
demonstrate the urgent need for enhanced security measures in the AV technology industry.
Given the potentially catastrophic effects of security flaws in these systems, ensuring their
safety is not merely a recommendation but an absolute necessity.
Therefore, future research must concentrate on the development and implementation
of resilient encryption protocols, the remediation of fundamental vulnerabilities, and the
design of inventive countermeasures that can adapt to an ever-changing threat landscape.
As we stand on the edge of a new era in which autonomous vehicles could transform
our transportation systems, it is crucial that we maintain a proactive stance regarding the
cyber security risks and safety challenges that accompany this technological revolution.
By establishing a robust and secure operational environment for unmanned autonomous
vehicles, we can not only ensure their operational effectiveness but also inspire public
confidence in their deployment. Nonetheless, it is crucial to remember that this attempt is a
process, not a destination. As the threat environment evolves, our defensive strategies must
also evolve. By maintaining alertness, conducting sustained research, and collaborating
across disciplines, we can effectively navigate this complex environment and safeguard the
promising future of autonomous vehicles from the cyber security threats they face.
Author Contributions:
A.G., A.K., L.T., C.K., P.K., N.S., G.K., and D.T., conceived of the idea,
designed and constructed the review article, analyzed the applications of autonomous vehicles,
drafted the initial manuscript, and revised the final manuscript. All authors have read and agreed to
the published version of the manuscript.
Funding: This research received no external funding.
Conflicts of Interest: The authors declare no conflict of interest.
Abbreviations
The following abbreviations are used in this manuscript:
AV Autonomous vehicle
EIAV Emotionally intelligent autonomous vehicle
LiDAR Light imaging detection and ranging
GPS Global positioning system
FAV Fully autonomous vehicle
ECU Engine control unit
CAN Controller area network
CIA Confidentiality, integrity, and availability
NHTSA National Highway Traffic Safety Administration
DOA Direction of arrival
IMU Inertial measurement unit
V2V Vehicle to vehicle
V2I Vehicle to infrastructure
VANET Vehicular ad hoc network
ABAKA Anonymous batch authentication and key agreement
CMAP Cooperative message authentication protocol
DoS Denial of service
DDoS Distributed denial of service
SPECS Secure and privacy-enhancing communications scheme
IBV Identity-based batch verification
RSU Roadside unit
TA Trusted authority
P2V Pedestrian to vehicle
J. Cybersecur. Priv. 2023,3535
V2P Vehicle to pedestrian
OBD Onboard diagnostic
OEM Original equipment manufacturer
DNN Deep neural network
CNN Convolutional neural network
FGSM Fast gradient sign method
JSMA Jacobian saliency-based adversarial attack
AEV Autonomous electric vehicle
AUV Autonomous underwater vehicle
AGV Autonomous guided vehicle
AAeV Autonomous aerial electric vehicle
OTA Over the air
AVSN Blockchain-enabled autonomous vehicular social network
CAV Connected autonomous vehicle
UAV Unmanned aerial vehicle
BCC Blockchain-based collaborative crowd sensing
AVN Autonomous vehicular network
UIoT Underwater Internet of Things
CACC Cooperative adaptive cruise control
References
1.
Guizzo, E. How Google’s Self-Driving Car Works. IEEE Spectrum Online. Available online: https://spectrum.ieee.org/how-
google-self-driving-car-works (accessed on 23 July 2023).
2.
Stiller, C.; Ziegler, J. 3D perception and planning for self-driving and cooperative automobiles. In Proceedings of the International
Multi-Conference on Systems, Signals & Devices, Chemnitz, Germany, 20–23 March 2012; pp. 1–7.
3.
Levinson, J.; Askeland, J.; Becker, J.; Dolson, J.; Held, D.; Kammel, S.; Kolter, J.Z.; Langer, D.; Pink, O.; Pratt, V.; et al. Towards
fully autonomous driving: Systems and algorithms. In Proceedings of the 2011 IEEE Intelligent Vehicles Symposium (IV),
Baden-Baden, Germany, 5–9 June 2011; pp. 163–168. [CrossRef]
4.
Petit, J.; Shladover, S.E. Potential Cyberattacks on Automated Vehicles. IEEE Trans. Intell. Transp. Syst.
2015
,16, 546–556.
[CrossRef]
5.
Jawhar, I.; Mohamed, N.; Wsmani, H. An Overview of Inter-Vehicular Communication Systems, Protocols and Middleware. J.
Netw. 2013,8, 12. [CrossRef]
6.
Petit, J.; Feiri, M.; Kargl, F. Revisiting attacker model for smart vehicles. In Proceedings of the IEEE 6th International Symposium
on Wireless Vehicular Communications (WiVeC 2014), Vancouver, BC, Canada, 14–15 September 2014; pp. 1–5.
7. Kerns, A.J.; Wesson, K.D.; Humphreys, T.E. A blueprint for civil GPS navigation message authentication. In Proceedings of the
2014 IEEE/ION Position, Location and Navigation Symposium-PLANS 2014, Monterey, CA, USA, 5–8 May 2014; pp. 262–269.
8. Uma, M.; Ganapathi, P. A survey on various cyber attacks and their classification. Int. J. Netw. Secur. 2013,15 , 390–396.
9.
Ahangar, M.N.; Ahmed, Q.Z.; Khan, F.A.; Hafeez, M. A Survey of Autonomous Vehicles: Enabling Communication Technologies
and Challenges. Sensors 2021,21, 706. [CrossRef] [PubMed]
10.
Ma, Y.; Wang, Z.; Yang, H.; Yang, L. Artificial intelligence applications in the development of autonomous vehicles: A survey.
IEEE/CAA J. Autom. Sin. 2020,7, 315–329. [CrossRef]
11.
Rasouli, A.; Tsotsos, J.K. Autonomous vehicles that interact with pedestrians: A survey of theory and practice. IEEE Trans. Intell.
Transp. Syst. 2019,21, 900–918. [CrossRef]
12.
Janai, J.; Güney, F.; Behl, A.; Geiger, A. Computer vision for autonomous vehicles: Problems, datasets and state of the art. Found.
Trends® Comput. Graph. Vis. 2020,12, 1–308. [CrossRef]
13.
Schwarting, W.; Alonso-Mora, J.; Rus, D. Planning and decision-making for autonomous vehicles. Annu. Rev. Control. Robot.
Auton. Syst. 2018,1, 187–210. [CrossRef]
14.
Parekh, D.; Poddar, N.; Rajpurkar, A.; Chahal, M.; Kumar, N.; Joshi, G.P.; Cho, W. A review on autonomous vehicles: Progress,
methods and challenges. Electronics 2022,11, 2162. [CrossRef]
15.
Vargas, J.; Alsweiss, S.; Toker, O.; Razdan, R.; Santos, J. An Overview of Autonomous Vehicles Sensors and Their Vulnerability to
Weather Conditions. Sensors 2021,21, 5397. [CrossRef]
16.
Tian, Y.; Pei, K.; Jana, S.; Ray, B. DeepTest: Automated Testing of Deep-Neural-Network-Driven Autonomous Cars. In Proceedings
of the 40th International Conference on Software Engineering, ICSE ’18, Gothenburg, Sweden, 27 May– 3 June 2018; pp. 303–314.
[CrossRef]
17.
Jing, P.; Xu, G.; Chen, Y.; Shi, Y.; Zhan, F. The Determinants behind the Acceptance of Autonomous Vehicles: A Systematic Review.
Sustainability 2020,12, 1719. [CrossRef]
18.
NHTSA. Automated Vehicles for Safety. Available online: www.nhtsa.gov/technology-innovation/automated-vehicles- safety
(accessed on 23 July 2023).
19. Salovey, P.; Mayer, J.D. Emotional intelligence. Imagin. Cogn. Personal. 1990,9, 185–211. [CrossRef]
J. Cybersecur. Priv. 2023,3536
20.
Ribeiro, M.A.; Gursoy, D.; Chi, O.H. Customer acceptance of autonomous vehicles in travel and tourism. J. Travel Res.
2022
,
61, 620–636. [CrossRef]
21.
Li, J.; Holländer, K.; Butz, A. Introducing automated driving to the generation 50+. In Proceedings of the 11th International
Conference on Automotive User Interfaces and Interactive Vehicular Applications: Adjunct Proceedings, Utrecht, The Netherlands,
21–25 September 2019; pp. 375–380.
22.
Hengstler, M.; Enkel, E.; Duelli, S. Applied artificial intelligence and trust—The case of autonomous vehicles and medical
assistance devices. Technol. Forecast. Soc. Chang. 2016,105, 105–120. [CrossRef]
23.
Sun, X.; Li, J.; Tang, P.; Zhou, S.; Peng, X.; Li, H.N.; Wang, Q. Exploring personalised autonomous vehicles to influence user trust.
Cogn. Comput. 2020,12, 1170–1186. [CrossRef]
24.
Amara, D.K.; Chebrolu, N.R.; R, V.; Kp, S. A Brief Survey on Autonomous Vehicle Possible Attacks, Exploits and Vulnerabilities.
arXiv 2018, arXiv:1810.04144.
25.
Hu, H.; Wei, N. A study of GPS jamming and anti-jamming. In Proceedings of the 2009 2nd International Conference on Power
Electronics and Intelligent Transportation System (PEITS), Shenzhen, China, 19–20 December 2009; pp. 388–391.
26.
Ahmad, M.; Akhtar, M.U. Impact and Detection of GPS Spoofing and Countermeasures against Spoofing. In Proceedings of
the 2nd International Conference on Computing, Mathematics and Engineering Technologies–iCoMET, Sukkur, Pakistan, 30–31
January 2019
27.
Parkinson, S.; Ward, P.; Wilson, K.; Miller, J. Cyber Threats Facing Autonomous and Connected Vehicles: Future Challenges.
IEEE Trans. Intell. Transp. Syst. 2017,18, 2898–2915. [CrossRef]
28.
O’Hanlon, B.W.; Psiaki, M.; Bhatti, J.A.; Shepard, D.P.; Humphreys, T.E. Real-Time GPS Spoofing Detection via Correlation of
Encrypted Signals. Navig.-J. Inst. Navig. 2013,60, 267–278. [CrossRef]
29.
Yang, Q.; Zhang, Y.; Tang, C.; Lian, J. A Combined Antijamming and Antispoofing Algorithm for GPS Arrays. Int. J. Antennas
Propag. 2019,2019, 8012569. [CrossRef]
30.
Nayegandhi, A. Lidar Technology Overview. In Proceedings of the US Geological Survey, St. Petersburg, FL, USA, 12 April 2019;
pp. 1–9.
31.
Petit, J.; Stottelaar, B.; Feirii, M. Remote Attacks on Automated Vehicles Sensors: Experiments on Camera and LiDAR. Black Hat
Eur. 2015,11, 995.
32.
Cao, Y.; Xiao, C.; Cyr, B.; Zhou, Y.; Park, W.; Rampazzi, S.; Chen, Q.A.; Fu, K.; Mao, Z.M. Adversarial Sensor Attack on
LiDAR-based Perception in Autonomous Driving. In Proceedings of the 2019 ACM SIGSAC Conference on Computer and
Communications Security, London, UK, 11–15 November 2019.
33. Changalvala, R.; Malik, H. LiDAR Data Integrity Verification for Autonomous Vehicle. IEEE Access 2019,18, 7. [CrossRef]
34.
Bahirat, K.; Prabhakaran, B. A study on lidar data forensics. In Proceedings of the 2017 IEEE International Conference on
Multimedia and Expo (ICME), Hong Kong, China, 10–14 July 2017; pp. 679–684.
35.
Hallyburton, R.S.; Pajic, M. Securing Autonomous Vehicles Under Partial-Information Cyber Attacks on LiDAR Data. arXiv
2023
,
arXiv:2303.03470.
36.
Bhupathiraju, S.H.V.; Sheldon, J.; Bauer, L.A.; Bindschaedler, V.; Sugawara, T.; Rampazzi, S. EMI-LiDAR: Uncovering Vulner-
abilities of LiDAR Sensors in Autonomous Driving Setting Using Electromagnetic Interference. In Proceedings of the 16th
ACM Conference on Security and Privacy in Wireless and Mobile Networks, Guildford, UK, 29 May–1 June 2023; pp. 329–340.
[CrossRef]
37.
Cao, Y.; Xiao, C.; Yang, D.; Fang, J.; Yang, R.; Liu, M.; Li, B. Adversarial objects against lidar-based autonomous driving systems.
arXiv 2019, arXiv:1907.05418.
38.
Haddrell, M.; Martin, K.M. Towards an Autonomous Vehicle Enabled Society: Cyber Attacks and Countermeasures. 2016.
Available online: http://book.itep.ru/depository/pilotless/RH-2016-autonomous- cars-Michael-Haddrell.pdf (accessed on 23
July 2023).
39.
Amoozadeh, M.; Raghuramu, A.; nee Chuah, C.; Ghosal, D.; Zhang, H.M.; Rowe, J.; Levitt, K. Security vulnerabilities of connected
vehicle streams and their impact on cooperative driving. IEEE Commun. Mag. 2015,53, 126–132. [CrossRef]
40.
Cheng, H.Y.; Jeng, B.S.; Tseng, P.T.; Fan, K.C. Lane Detection with Moving Vehicles in the Traffic Scenes. IEEE Trans. Intell. Transp.
Syst. 2006,7, 571–582. [CrossRef]
41.
Bahlmann, C.; Zhu, Y.; Ramesh, V.; Pellkofer, M.; Koehler, T. A system for traffic sign detection, tracking, and recognition using
color, shape, and motion information. In Proceedings of the IEEE Proceedings, Intelligent Vehicles Symposium, Las Vegas, NV,
USA, 6–8 June 2005; pp. 255–260.
42.
Eum, S.; Jung, H.G. Enhancing Light Blob Detection for Intelligent Headlight Control Using Lane Detection. IEEE Trans. Intell.
Transp. Syst. 2013,14, 255–260. [CrossRef]
43.
Gomes, L. Hidden Obstacles for Google’s Self-Driving Cars. Available online: www.technologyreview.com/s/530276/hidden-
obstacles-for-googles-self-driving-cars/ (accessed on 23 July 2023).
44.
Koscher, K.; Czeskis, A.; Roesner, F.; Patel, S.; Kohno, T.; Checkoway, S.; McCoy, D.; Kantor, B.; Anderson, D.; Shacham, H.; et al.
Experimental Security Analysis of a Modern Automobile. In Proceedings of the I2010 IEEE Symposium on Security and Privacy,
Berkeley/Oakland, CA, USA, 16–19 May 2010; pp. 447–462.
45.
Joo, J.; Park, M.C.; Han, D.S.; Pejovic, V. Deep learning-based channel prediction in realistic vehicular communications. IEEE
Access 2019,7, 27846–27858. [CrossRef]
J. Cybersecur. Priv. 2023,3537
46.
Mehdizadeh, A.; Cai, M.; Hu, Q.; Alamdar Yazdi, M.A.; Mohabbati-Kalejahi, N.; Vinel, A.; Rigdon, S.E.; Davis, K.C.; Megahed,
F.M. A Review of Data Analytic Applications in Road Traffic Safety. Part 1: Descriptive and Predictive Modeling. Sensors
2020
,
20, 1107. [CrossRef]
47.
Zahedi, F.; Farzaneh, N. An evolutionary game theory–based security model in vehicular ad hoc networks. Int. J. Commun. Syst.
2020,33, e4290. [CrossRef]
48.
Tesei, A.; Luise, M.; Pagano, P.; Ferreira, J. Secure Multi-access Edge Computing Assisted Maneuver Control for Autonomous
Vehicles. In Proceedings of the 2021 IEEE 93rd Vehicular Technology Conference (VTC2021-Spring), Helsinki, Finland, 25–28
April 2021; pp. 1–6. [CrossRef]
49.
Shrivastava, D.; Pandey, A. A Study of Sybil and Temporal Attacks in Vehicular Ad-Hoc Networks: Types, Challenges, and
Impacts. Int. J. Comput. Appl. Technol. Res. 2014,3, 284–291. [CrossRef]
50.
Huang, J.L.; Yeh, L.Y.; Chien, H.Y. ABAKA: An Anonymous Batch Authenticated and Key Agreement Scheme for Value-Added
Services in Vehicular Ad Hoc Networks. IEEE Trans. Veh. Technol. 2011,60, 248–262. [CrossRef]
51.
Chen, C.; Wang, X.; Han, W.; Zang, B. A Robust Detection of the Sybil Attack in Urban VANETs. In Proceedings of the 2009
29th IEEE International Conference on Distributed Computing Systems Workshops, Montreal, QC, Canada, 22–26 June 2009; pp.
270–276.
52.
Hao, Y.; Tang, J.; Cheng, Y. Cooperative Sybil Attack Detection for Position Based Applications in Privacy Preserved VANETs. In
Proceedings of the 2011 IEEE Global Telecommunications Conference-GLOBECOM 2011, Houston, TX, USA, 5–9 December 2011;
pp. 1–5.
53.
Hao, Y.; Cheng, Y.; Zhou, C.; Song, W. A Distributed Key Management Framework with Cooperative Message Authentication in
VANETs. IEEE J. Sel. Areas Commun. 2011,29, 616–629. [CrossRef]
54.
Zhou, T.; Choudhury, R.R.; Ning, P.; Chakrabarty, K. P2DAP—Sybil Attacks Detection in Vehicular Ad Hoc Networks. IEEE J. Sel.
Areas Commun. 2011,29, 582–594. [CrossRef]
55.
Triki, B.; Rekhis, S.; Chammem, M.; Boudriga, N. A privacy preserving solution for the protection against sybil attacks in
vehicular ad hoc networks. In Proceedings of the 6th Joint IFIP Wireless and Mobile Networking Conference (WMNC), Dubai,
United Arab Emirates, 23–25 April 2013; pp. 1–8.
56.
Grover, J.; Laxmi, V.; Gaur, M.S. Sybil attack detection in VANET using neighbouring vehicles. Int. J. Secur. Netw.
2014
,9, 222–233.
[CrossRef]
57.
Pathre, A.; Agrawal, C.; Jain, A. Identification of malicious vehicle in vanet environment from ddos attack. J. Glob. Res. Comput.
Sci. 2013,4, 1–5.
58.
Pathre, A.; Agrawal, C.; Jain, A. A novel defense scheme against DDOS attack in VANET. In Proceedings of the 2013 Tenth
International Conference on Wireless and Optical Communications Networks (WOCN), Bhopal, India, 26–28 July 2013; pp. 1–5.
59.
Malla, A.M.; Sahu, R.K. Security Attacks with an Effective Solution for DOS Attacks in VANET. Int. J. Comput. Appl.
2013
,66,
975–8887.
60.
He, L.; Zhu, W.T. Mitigating DoS attacks against signature-based authentication in VANETs. In Proceedings of the 2012 IEEE
International Conference on Computer Science and Automation Engineering (CSAE), Zhangjiajie, China, 25–27 May 2012;
pp. 261–265.
61. Verma, K.; Hasbullah, H.; Kumar, A. Prevention of DoS attacks in VANET. Wirel. Pers. Commun. 2013,73, 95–126. [CrossRef]
62.
Safi, S.M.; Movaghar, A.; Mohammadizadeh, M. A novel approach for avoiding wormhole attacks in VANET. In Proceedings of
the 2009 First Asian Himalayas International Conference on Internet, Kathmandu, Nepal, 28–30 October 2009; pp. 1–6.
63. Zhang, C.; Lu, R.; Lin, X.; Ho, P.; Shen, X. An Efficient Identity-Based Batch Verification Scheme for Vehicular Sensor Networks.
In Proceedings of the IEEE INFOCOM 2008-The 27th Conference on Computer Communications, Phoenix, AZ, USA; 2008;
pp. 246–250.
64.
Chim, T.W.; Yiu, S.; Hui, L.C.K.; Li, V.O.K. SPECS: Secure and privacy enhancing communications schemes for VANETs. Ad Hoc
Netw. 2011,9, 189–203. [CrossRef]
65.
Kim, T.; Studer, A.; Dubey, R. VANET alert endorsement using multi-source filters. In Proceedings of the Seventh International
Workshop on Vehicular Ad Hoc Networks, VANET 2010, Chicago, IL, USA, 24 September 2010.
66.
Lin, X.; Sun, X.; Ho, P.H.; Shen, X. GSIS: A Secure and Privacy-Preserving Protocol for Vehicular Communications. IEEE Trans.
Veh. Technol. 2007,56, 3442–3456.
67.
Papadimitratos, P.; Buttyan, L.; Hubaux, J.P.; Kargl, F.; Kung, A.; Raya, M. Architecture for Secure and Private Vehicular
Communications. In Proceedings of the 2007 7th International Conference on ITS Telecommunications, Sophia Antipolis, France,
6–8 June 2007 ; pp. 1–6.
68.
Hussein, A.; García, F.; Armingol, J.M.; Olaverri-Monreal, C. P2V and V2P communication for Pedestrian warning on the basis of
Autonomous Vehicles. In Proceedings of the 2016 IEEE 19th International Conference on Intelligent Transportation Systems
(ITSC), Rio de Janeiro, Brazil, 1–4 November 2016; pp. 2034–2039.
69.
Can Driverless Vehicles Be Hacked? Available online: https://www.hlmlawfirm.com/blog/can-driverless-vehicles-be-hacked/
(accessed on 19 July 2023).
70.
HackerNoon. Available online: https://hackernoon.com/how-to-hack-self-driving-cars-vulnerabilities-in-autonomous-
vehicles-jh3r37cz (accessed on 19 July 2023).
J. Cybersecur. Priv. 2023,3538
71.
Algarni, A.; Thayananthan, V. Autonomous Vehicles: The Cybersecurity Vulnerabilities and Countermeasures for Big Data
Communication. Symmetry 2022,14, 2494. [CrossRef]
72.
Kumar, K.N.; Vishnu, C.; Mitra, R.; Mohan, C.K. Black-box adversarial attacks in autonomous vehicle technology. In Proceedings
of the 2020 IEEE Applied Imagery Pattern Recognition Workshop (AIPR), Washington, DC, USA, 13–15 October 2020; pp. 1–7.
73.
Santa, J.; Bernal-Escobedo, L.; Sanchez-Iborra, R. On-board unit to connect personal mobility vehicles to the IoT. Procedia Comput.
Sci. 2020,175, 173–180. [CrossRef]
74.
Chang, X.; Li, H.; Rong, J.; Huang, Z.; Chen, X.; Zhang, Y. Effects of on-board unit on driving behavior in connected vehicle traffic
flow. J. Adv. Transp. 2019,2019, 1–12. [CrossRef]
75.
Zhang, B.; Wang, X.; Xie, R.; Li, C.; Zhang, H.; Jiang, F. A reputation mechanism based Deep Reinforcement Learning and
blockchain to suppress selfish node attack motivation in Vehicular Ad-Hoc Network. Future Gener. Comput. Syst.
2023
,139, 17–28.
[CrossRef]
76.
Karmakar, G.; Chowdhury, A.; Das, R.; Kamruzzaman, J.; Islam, S. Assessing trust level of a driverless car using deep learning.
IEEE Trans. Intell. Transp. Syst. 2021,22, 4457–4466. [CrossRef]
77.
Zhang, Y.; Ge, B.; Li, X.; Shi, B.; Li, B. Controlling a Car Through OBD Injection. In Proceedings of the 2016 IEEE 3rd International
Conference on Cyber Security and Cloud Computing (CSCloud), Beijing, China, 25–27 June 2016; pp. 26–29.
78.
Yan, W. A two-year survey on security challenges in automotive threat landscape. In Proceedings of the 2015 International
Conference on Connected Vehicles and Expo (ICCVE), Shenzhen, China, 19–23 October 2015; pp. 185–189.
79.
Yadav, A.; Bose, G.; Bhange, R.; Kapoor, K. Security, Vulnerability and Protection of Vehicular On-board Diagnostics. Int. J. Secur.
Its Appl. 2016,10, 405–422. [CrossRef]
80.
Oka, D.K.; Larson, U.E. Conducting Forensic Investigations of Cyber Attacks on Automobile In-Vehicle Networks. Int. J. Digit.
Crime Forensics 2009,2, 28–41.
81.
Vallance, C. Car Hack Uses Digital-Radio Broadcasts to Seize Control. 22 July 2015. Available online: www.bbc.com/news/
technology-33622298 (accessed on 23 July 2023).
82.
Checkoway, S.; McCoy, D.; Kantor, B.; Anderson, D.; Shacham, H.; Savage, S.; Koscher, K.; Czeskis, A.; Roesner, F.; Kohno, T.
Comprehensive experimental analyses of automotive attack surfaces. In Proceedings of the 20th USENIX Conference on Security,
San Francisco, CA, USA, 8–12 August 2011.
83.
Luo, F.; Hou, S. Security Mechanisms Design of Automotive Gateway Firewall; Technical Report; SAE Technical Paper; SAE:
Warrendale, PA, USA , 2019.
84.
Zhang, H.; Meng, X.; Zhang, X.; Liu, Z. CANsec: A Practical in-Vehicle Controller Area Network Security Evaluation Tool.
Sensors 2020,20, 4900. [CrossRef] [PubMed]
85.
Duan, X.; Yan, H.; Tian, D.; Zhou, J.; Su, J.; Hao, W. In-Vehicle CAN Bus Tampering Attacks Detection for Connected and
Autonomous Vehicles Using an Improved Isolation Forest Method. IEEE Trans. Intell. Transp. Syst.
2023
,24, 2122–2134. [CrossRef]
86.
Hoque, M.A.; Hossain, M.; Hasan, R. BenchAV: A Security Benchmarking Framework for Autonomous Driving. In Proceedings
of the 2022 IEEE 19th Annual Consumer Communications & Networking Conference (CCNC), Las Vegas, NV, USA, 8–11 January
2022; pp. 729–730. [CrossRef]
87.
Brocklehurst, C.; Radenkovic, M. Resistance to Cybersecurity Attacks in a Novel Network for Autonomous Vehicles. J. Sens.
Actuator Netw. 2022,11, 35. [CrossRef]
88.
Qurashi, J.M.; Ikram, M.J.; Jambi, K.; Eassa, F.E.; Khemakhem, M. Autonomous Vehicles: Security Challenges and Game
theory-based Countermeasures. In Proceedings of the 2023 1st International Conference on Advanced Innovations in Smart
Cities (ICAISC), Jeddah, Saudi Arabia, 23–25 January 2023; pp. 1–6. [CrossRef]
89.
Yadav, N.; Ansar, S.A.; Chaurasia, P.K. Review of Attacks on Connected and Autonomous Vehicles (CAV) and their Existing
Solutions. In Proceedings of the 2022 10th International Conference on Reliability, Infocom Technologies and Optimization
(Trends and Future Directions) (ICRITO), Noida, India, 13–14 October 2022; pp. 1–6. [CrossRef]
90. Goyal, S.B. Autonomous Vehicles: Improving Cyber Security. Int. J. Adv. Res. Technol. Innov. 2022,4, 118–126 .
91.
Kamal, M.; Kyrkou, C.; Piperigkos, N.; Papandreou, A.; Kloukiniotis, A.; Casademont, J.; Mateu, N.P.; Castillo, D.B.; Rodriguez,
R.D.; Durante, N.G.; et al. A Comprehensive Solution for Securing Connected and Autonomous Vehicles. In Proceedings of the
2022 Design, Automation & Test in Europe Conference & Exhibition (DATE), Antwerp, Belgium, 14–23 March 2022; pp. 790–795.
[CrossRef]
92.
Kennedy, C. New Threats to Vehicle Safety: How Cybersecurity Policy Will Shape the Future of Autonomous Vehicles. Mich.
Telecommun. Technol. Law Rev. 2017,23, 343–356.
93.
Wang, Z.; Wei, H.; Wang, J.; Zeng, X.; Chang, Y. Security Issues and Solutions for Connected and Autonomous Vehicles in a
Sustainable City: A Survey. Sustainability 2022,14, 12409. [CrossRef]
94.
Shangguan, L.; Chour, K.; Ko, W.H.; Kim, J.; Kamath, G.K.; Satchidanandan, B.; Gopalswamy, S.; Kumar, P.R. Dynamic
Watermarking for Cybersecurity of Autonomous Vehicles. IEEE Trans. Ind. Electron. 2023,70, 11735–11743. [CrossRef]
95. Szegedy, C.; Zaremba, W.; Sutskever, I.; Bruna, J.; Erhan, D.; Goodfellow, I.; Fergus, R. Intriguing properties of neural networks.
arXiv 2014, arXiv:1312.6199
96. Goodfellow, I.; Shlens, J.; Szegedy, C. Explaining and Harnessing Adversarial Examples. arXiv 2014, arXiv:1412.6572.
97.
Kurakin, A.; Goodfellow, I.J.; Bengio, S. Adversarial examples in the physical world. In Artificial Intelligence Safety and Security;
Chapman and Hall/CRC: London, UK, 2018; pp. 99–112.
J. Cybersecur. Priv. 2023,3539
98.
Engstrom, L.; Tsipras, D.; Schmidt, L.; Madry, A. A Rotation and a Translation Suffice: Fooling CNNs with Simple Transformations.
In Proceedings of the ICLR 2019 Conference Blind Submission, New Orleans, LA, USA, 6–9 May 2019.
99.
Pei, K.; Cao, Y.; Yang, J.; Jana, S. Towards Practical Verification of Machine Learning: The Case of Computer Vision Systems.
arXiv 2017, arXiv:1712.01785.
100.
Liu, Y.; Ma, S.; Aafer, Y.; Lee, W.C. Trojaning Attack on Neural Networks. In Proceedings of the Network and Distributed System
Security Symposium, Diego, CA, USA, 18–21 February 2018.
101.
Papernot, N.; McDaniel, P.; Jha, S.; Fredrikson, M. The Limitations of Deep Learning in Adversarial Settings. In Proceedings of
the 1st IEEE European Symposium on Security & Privacy, Saarbrucken, Germany, 21–24 March 2016.
102. Papernot, N.; McDaniel, P.; Goodfellow, I.; Jha, S.; Celik, Z.B.; Swami, A. Practical Black-Box Attacks against Machine Learning.
In Proceedings of the 2017 ACM Asia Conference on Computer and Communications Security, Abu Dhabi, United Arab Emirates,
2–6 April 2017.
103.
Carlini, N.; Wagner, D. Towards Evaluating the Robustness of Neural Networks. In Proceedings of the 2017 IEEE Symposium on
Security and Privacy (SP), San Jose, CA, USA, 22–26 May 2017; pp. 39–57.
104.
Carlini, N.; Wagner, D. Adversarial Examples Are Not Easily Detected: Bypassing Ten Detection Methods. In Proceedings of the
10th ACM Workshop on Artificial Intelligence and Security, Dallas, TX, USA, 3 November 2017; pp. 3–14.
105. Brown, T.B.; Mané, D.; Roy, A.; Abadi, M.; Gilmer, J. Adversarial patch. arXiv 2017, arXiv:1712.09665.
106.
Su, J.; Vargas, D.V.; Kouichir, S. One pixel attack for fooling deep neural networks. IEEE Trans. Evol. Comput.
2017
,23, 828–841.
[CrossRef]
107.
Fényes, D.; Németh, B.; Gáspár, P. Design of LPV control for autonomous vehicles using the contributions of big data analysis.
Int. J. Control 2022,95, 1802–1813. [CrossRef]
108.
Familsamavati, S.; Yari, P.; Salehian, S.; Salehian, R.; Abbasi, M.; Khosravi, M.R. The Role of Big Data and Smart Technologies in
Autonomous Vehicles. In Proceedings of the 5th International Conference on Future Networks & Distributed Systems, ICFNDS
2021, New York, NY, USA, 15–16 December 2021; pp. 641–646. [CrossRef]
109.
Yoo, A.; Shin, S.; Lee, J.; Moon, C. Implementation of a Sensor Big Data Processing System for Autonomous Vehicles in the C-ITS
Environment. Appl. Sci. 2020,10, 7858. [CrossRef]
110.
Karras, A.; Karras, C.; Schizas, N.; Avlonitis, M.; Sioutas, S. AutoML with Bayesian Optimizations for Big Data Management.
Information 2023,14, 223. [CrossRef]
111.
Karras, C.; Karras, A.; Giotopoulos, K.C.; Avlonitis, M.; Sioutas, S. Consensus Big Data Clustering for Bayesian Mixture Models.
Algorithms 2023,16, 245. [CrossRef]
112.
Karras, C.; Karras, A.; Tsolis, D.; Giotopoulos, K.C.; Sioutas, S. Distributed Gibbs Sampling and LDA Modelling for Large
Scale Big Data Management on PySpark. In Proceedings of the 2022 7th South-East Europe Design Automation, Computer
Engineering, Computer Networks and Social Media Conference (SEEDA-CECNSM), Ioannina, Greece, 23–25 September 2022; pp.
1–8. [CrossRef]
113. Samoladas, D.; Karras, C.; Karras, A.; Theodorakopoulos, L.; Sioutas, S. Tree Data Structures and Efficient Indexing Techniques
for Big Data Management: A Comprehensive Study. In Proceedings of the 26th Pan-Hellenic Conference on Informatics, PCI ’22,
Athens, Greece, 25–27 November 2022; pp. 123–132. [CrossRef]
114.
Nguyen, H.; Nguyen, T.; Leppänen, T.; Partala, J.; Pirttikangas, S. Situation awareness for autonomous vehicles using blockchain-
based service cooperation. In Proceedings of the International Conference on Advanced Information Systems Engineering; Springer:
Berlin/Heidelberg, Germany, 2022; pp. 501–516.
115.
Kianersi, D.; Uppalapati, S.; Bansal, A.; Straub, J. Evaluation of a Reputation Management Technique for Autonomous Vehicles.
Future Internet 2022,14, 31. [CrossRef]
116.
Baza, M.; Nabil, M.; Lasla, N.; Fidan, K.; Mahmoud, M.; Abdallah, M. Blockchain-based firmware update scheme tailored
for autonomous vehicles. In Proceedings of the 2019 IEEE Wireless Communications and Networking Conference (WCNC),
Marrakesh, Morocco, 15–18 April 2019; pp. 1–7.
117.
Oham, C.; Kanhere, S.S.; Jurdak, R.; Jha, S. A blockchain based liability attribution framework for autonomous vehicles. arXiv
2018, arXiv:1802.05050.
118.
Shivers, R.M. Toward a Secure and Decentralized Blockchain-Based Ride-Hailing Platform for Autonomous Vehicles. PhD Thesis,
Tennessee Technological University, Cookeville, TN, USA, 2019.
119.
Mollah, M.B.; Zhao, J.; Niyato, D.; Guan, Y.L.; Yuen, C.; Sun, S.; Lam, K.Y.; Koh, L.H. Blockchain for the internet of vehicles
towards intelligent transportation systems: A survey. IEEE Internet Things J. 2020,8, 4157–4185. [CrossRef]
120.
Karras, A.; Karras, C.; Drakopoulos, G.; Tsolis, D.; Mylonas, P.; Sioutas, S. SAF: A Peer to Peer IoT LoRa System for Smart Supply
Chain in Agriculture. In Artificial Intelligence Applications and Innovations; Maglogiannis, I., Iliadis, L., Macintyre, J., Cortez, P.,
Eds.; Springer: Cham, Switzerland, 2022; pp. 41–50.
121.
Kamble, N.; Gala, R.; Vijayaraghavan, R.; Shukla, E.; Patel, D. Using Blockchain in Autonomous Vehicles. In Artificial Intelligence
and Blockchain for Future Cybersecurity Applications; Maleh, Y., Baddi, Y., Alazab, M., Tawalbeh, L., Romdhani, I., Eds.; Springer
International Publishing: Cham, Switzerland, 2021; pp. 285–305. [CrossRef]
122.
Show, A.K.; Kumar, A.; Singhal, A.; Gayathri, N.; Vengatesan, K. Future blockchain technology for autonomous applications/
autonomous vehicle. In Opportunities and Challenges for Blockchain Technology in Autonomous Vehicles; IGI Global: Hershey, PA,
USA, 2021; pp. 165–177.
J. Cybersecur. Priv. 2023,3540
123.
Pedrosa, A.R.; Pau, G. ChargeltUp: On Blockchain-Based Technologies for Autonomous Vehicles. In Proceedings of the 1st
Workshop on Cryptocurrencies and Blockchains for Distributed Systems, CryBlock’18, New York, NY, USA, 15 June 2018;
pp. 87–92. [CrossRef]
124.
Jain, S.; Ahuja, N.J.; Srikanth, P.; Bhadane, K.V.; Nagaiah, B.; Kumar, A.; Konstantinou, C. Blockchain and Autonomous Vehicles:
Recent Advances and Future Directions. IEEE Access 2021,9, 130264–130328. [CrossRef]
125.
Bathla, G.; Bhadane, K.V.; Singh, R.K.; Kumar, R.; Aluvalu, D.R.; Krishnamurthi, R.; Kumar, A.; Thakur, R.N.; Basheer, S.
Autonomous Vehicles and Intelligent Automation: Applications, Challenges, and Opportunities. Mob. Inf. Syst.
2022
,2022,
7632892. [CrossRef]
126.
Yeasmin, S.; Haque, A. A Multi-Factor Authenticated Blockchain-Based OTA Update Framework for Connected Autonomous
Vehicles. In Proceedings of the 2021 IEEE 94th Vehicular Technology Conference (VTC2021-Fall), Norman, OK, USA, 27–30
September 2021; pp. 1–6. [CrossRef]
127.
Narbayeva, S.; Bakibayev, T.; Abeshev, K.; Makarova, I.; Shubenkova, K.; Pashkevich, A. Blockchain technology on the way of
autonomous vehicles development. Transp. Res. Procedia 2020,44, 168–175. [CrossRef]
128.
Wang, Y.; Su, Z.; Zhang, K.; Benslimane, A. Challenges and Solutions in Autonomous Driving: A Blockchain Approach. IEEE
Netw. 2020,34, 218–226. [CrossRef]
129. Alladi, T.; Chamola, V.; Sahu, N.; Guizani, M. Applications of blockchain in unmanned aerial vehicles: A review. Veh. Commun.
2020,23, 100249. .: 10.1016/j.vehcom.2020.100249. [CrossRef]
130.
Hui, Y.; Huang, Y.; Su, Z.; Luan, T.H.; Cheng, N.; Xiao, X.; Ding, G. BCC: Blockchain-Based Collaborative Crowdsensing in
Autonomous Vehicular Networks. IEEE Internet Things J. 2022,9, 4518–4532. [CrossRef]
131.
Bendiab, G.; Hameurlaine, A.; Germanos, G.; Kolokotronis, N.; Shiaeles, S. Autonomous Vehicles Security: Challenges and
Solutions Using Blockchain and Artificial Intelligence. IEEE Trans. Intell. Transp. Syst. 2023,24, 3614–3637. [CrossRef]
132.
Aloqaily, M.; Hussain, R.; Khalaf, D.; Slehat, D.; Oracevic, A. On the Role of Futuristic Technologies in Securing UAV-Supported
Autonomous Vehicles. IEEE Consum. Electron. Mag. 2022,11, 93–105. [CrossRef]
133.
Jha, S.; Jha, N.; Prashar, D.; Ahmad, S.; Alouffi, B.; Alharbi, A. Integrated IoT-Based Secure and Efficient Key Management
Framework Using Hashgraphs for Autonomous Vehicles to Ensure Road Safety. Sensors 2022,22, 2529. [CrossRef] [PubMed]
134.
Kuzmin, A.; Znak, E. Blockchain-base structures for a secure and operate network of semi-autonomous Unmanned Aerial
Vehicles. In Proceedings of the 2018 IEEE International Conference on Service Operations and Logistics, and Informatics (SOLI),
Singapore, 31 July 2018–2 August 2018; pp. 32–37. [CrossRef]
135.
Rajendar, S.; Thangavel, U.; Devendran, S.; Selvi, V.; Muthumanickam, S.S. Blockchain for Securing Autonomous Vehicles. In
Proceedings of the 2023 Second International Conference on Electronics and Renewable Systems (ICEARS), Tamil Nadu, India,
2–4 March 2023; pp. 713–717. [CrossRef]
136.
Gupta, R.; Tanwar, S.; Kumar, N.; Tyagi, S. Blockchainbased security attack resilience schemes for autonomous vehicles in
industry 4.0: A systematic review. Comput. Electr. Eng. 2020,86, 106717. [CrossRef]
137.
Reyna, A.; Martín, C.; Chen, J.; Soler, E.; Díaz, M. On blockchain and its integration with IoT. Challenges and opportunities.
Future Gener. Comput. Syst. 2018,88, 173–190. .: 10.1016/j.future.2018.05.046. [CrossRef]
138.
Jabbar, R.; Dhib, E.; Said, A.B.; Krichen, M.; Fetais, N.; Zaidan, E.; Barkaoui, K. Blockchain Technology for Intelligent Transporta-
tion Systems: A Systematic Literature Review. IEEE Access 2022,10, 20995–21031. [CrossRef]
139. Jiang, T.; Fang, H.; Wang, H. Blockchain-based internet of vehicles: Distributed network architecture and performance analysis.
IEEE Internet Things J. 2018,6, 4640–4649. [CrossRef]
140.
Zhang, X.; Chen, X. Data security sharing and storage based on a consortium blockchain in a vehicular ad hoc network. Ieee
Access 2019,7, 58241–58254. [CrossRef]
141.
Singh, S.K.; Park, J.H.; Sharma, P.K.; Pan, Y. BIIoVT: Blockchain-based secure storage architecture for intelligent internet of
vehicular things. IEEE Consum. Electron. Mag. 2021,11, 75–82. [CrossRef]
142.
Yin, Y.; Li, Y.; Ye, B.; Liang, T.; Li, Y. A blockchain-based incremental update supported data storage system for intelligent vehicles.
IEEE Trans. Veh. Technol. 2021,70, 4880–4893. [CrossRef]
143.
Kakkar, R.; Gupta, R.; Agrawal, S.; Tanwar, S.; Sharma, R. Blockchain-based secure and trusted data sharing scheme for
autonomous vehicle underlying 5G. J. Inf. Secur. Appl. 2022,67, 103179. [CrossRef]
144.
Nair, M.M.; Tyagi, A.K. Preserving privacy using blockchain technology in autonomous vehicles. In Proceedings of the International
Conference on Network Security and Blockchain Technology; Springer: Berlin/Heidelberg, Germany, 2021; pp. 237–248.
145.
Singh, M.; Kim, S. Introduce reward-based intelligent vehicles communication using blockchain. In Proceedings of the 2017
International SoC Design Conference (ISOCC), Seoul, Republic of Korea, 5–8 November 2017; pp. 15–16.
146.
Rowan, S.; Clear, M.; Gerla, M.; Huggard, M.; Goldrick, C.M. Securing vehicle to vehicle communications using blockchain
through visible light and acoustic side-channels. arXiv 2017, arXiv:1704.02553.
147.
Yang, Z.; Yang, K.; Lei, L.; Zheng, K.; Leung, V.C. Blockchain-based decentralized trust management in vehicular networks. IEEE
Internet Things J. 2018,6, 1495–1505. [CrossRef]
148.
Mitra, S.; Bose, S.; Gupta, S.S.; Chattopadhyay, A. Secure and tamper-resilient distributed ledger for data aggregation in
autonomous vehicles. In Proceedings of the 2018 IEEE Asia Pacific Conference on Circuits and Systems (APCCAS), Chengdu,
China, 26–30 October 2018; pp. 548–551.
J. Cybersecur. Priv. 2023,3541
149.
Rathee, G.; Sharma, A.; Iqbal, R.; Aloqaily, M.; Jaglan, N.; Kumar, R. A Blockchain Framework for Securing Connected and
Autonomous Vehicles. Sensors 2019,19, 3165. [CrossRef]
150.
Oham, C.; Michelin, R.A.; Jurdak, R.; Kanhere, S.S.; Jha, S. B-FERL: Blockchain based framework for securing smart vehicles. Inf.
Process. Manag. 2021,58, 102426. [CrossRef]
151.
Reebadiya, D.; Rathod, T.; Gupta, R.; Tanwar, S.; Kumar, N. Blockchain-based secure and intelligent sensing scheme for
autonomous vehicles activity tracking beyond 5g networks. Peer- Netw. Appl. 2021,14, 2757–2774. [CrossRef]
152.
Wang, J.; Liu, Y.; Niu, S.; Song, H. Lightweight blockchain assisted secure routing of swarm UAS networking. Comput. Commun.
2021,165, 131–140. [CrossRef]
153.
Kamal, M.; Srivastava, G.; Tariq, M. Blockchain-based lightweight and secured v2v communication in the internet of vehicles.
IEEE Trans. Intell. Transp. Syst. 2020,22, 3997–4004. [CrossRef]
154.
Ali, A.; Iqbal, M.M.; Jabbar, S.; Asghar, M.N.; Raza, U.; Al-Turjman, F. VABLOCK: A blockchain-based secure communication in
V2V network using icn network support technology. Microprocess. Microsyst. 2022,93, 104569. [CrossRef]
155.
Cebe, M.; Erdin, E.; Akkaya, K.; Aksu, H.; Uluagac, S. Block4forensic: An integrated lightweight blockchain framework for
forensics applications of connected vehicles. IEEE Commun. Mag. 2018,56, 50–57. [CrossRef]
156.
Alphand, O.; Amoretti, M.; Claeys, T.; Dall’Asta, S.; Duda, A.; Ferrari, G.; Rousseau, F.; Tourancheau, B.; Veltri, L.; Zanichelli, F.
IoTChain: A blockchain security architecture for the Internet of Things. In Proceedings of the 2018 IEEE Wireless Communications
and Networking Conference (WCNC), Barcelona, Spain, 15–18 April 2018; pp. 1–6.
157.
Li, C.; Palanisamy, B. Privacy in internet of things: From principles to technologies. IEEE Internet Things J.
2018
,6, 488–505.
[CrossRef]
158.
Davi, L.; Hatebur, D.; Heisel, M.; Wirtz, R. Combining safety and security in autonomous cars using blockchain technologies. In
Proceedings of the Computer Safety, Reliability, and Security: SAFECOMP 2019 Workshops, ASSURE, DECSoS, SASSUR, STRIVE,
and WAISE, Turku, Finland, 10 September 2019; Proceedings 38; Springer: Berlin/Heidelberg, Germany, 2019; pp. 223–234.
159.
Rathore, H.; Samant, A.; Jadliwala, M. TangleCV: A distributed ledger technique for secure message sharing in connected vehicles.
ACM Trans. Cyber-Phys. Syst. 2020,5, 1–25. [CrossRef]
160.
Li, M.; Chen, Y.; Lal, C.; Conti, M.; Alazab, M.; Hu, D. Eunomia: Anonymous and secure vehicular digital forensics based on
blockchain. IEEE Trans. Dependable Secur. Comput. 2021,20, 225–241. [CrossRef]
161.
Yao, Q.; Li, T.; Yan, C.; Deng, Z. Accident responsibility identification model for Internet of Vehicles based on lightweight
blockchain. Comput. Intell. 2023,39, 58–81. [CrossRef]
162.
Kang, J.; Xiong, Z.; Niyato, D.; Ye, D.; Kim, D.I.; Zhao, J. Toward secure blockchain-enabled internet of vehicles: Optimizing
consensus management using reputation and contract theory. IEEE Trans. Veh. Technol. 2019,68, 2906–2920. [CrossRef]
163.
Abbes, S.; Rekhis, S. A blockchain-based solution for reputation management in IoV. In Proceedings of the 2021 International
Wireless Communications and Mobile Computing (IWCMC), Harbin City, China, 28 June–2 July 2021; pp. 1129–1134.
164.
Feng, L.; Yang, Z.; Guo, S.; Qiu, X.; Li, W.; Yu, P. Two-Layered Blockchain Architecture for Federated Learning Over the Mobile
Edge Network. IEEE Netw. 2022,36, 45–51. [CrossRef]
165.
Bhattacharya, P.; Shukla, A.; Tanwar, S.; Kumar, N.; Sharma, R. 6Blocks: 6G-enabled trust management scheme for decentralized
autonomous vehicles. Comput. Commun. 2022,191, 53–68. [CrossRef]
166.
Mushtaq, A.; Haq, I.; Sarwar, M.A.; Khan, A.; Shafiq, O. Traffic Management of Autonomous Vehicles using Policy Based Deep
Reinforcement Learning and Intelligent Routing. arXiv 2022, arXiv:2206.14608.
167.
Elayan, H.; Aloqaily, M.; Salameh, H.B.; Guizani, M. Intelligent Cooperative Health Emergency Response System in Autonomous
Vehicles. In Proceedings of the 2021 IEEE 46th Conference on Local Computer Networks (LCN), Edmonton, AB, Canada, 4–7
October 2021; pp. 293–298. [CrossRef]
168.
Kusari, A.; Li, P.; Yang, H.; Punshi, N.; Rasulis, M.; Bogard, S.; LeBlanc, D.J. Enhancing SUMO simulator for simulation based
testing and validation of autonomous vehicles. In Proceedings of the 2022 IEEE Intelligent Vehicles Symposium (IV), Aachen,
Germany, 5–9 June 2022; pp. 829–835. [CrossRef]
169.
Qiu, T.; Zhao, Z.; Zhang, T.; Chen, C.; Chen, C.L.P. Underwater Internet of Things in Smart Ocean: System Architecture and Open
Issues. IEEE Trans. Ind. Inform. 2020,16, 4297–4307. [CrossRef]
170.
Ganin, D.V.; Gladkikh, A.A.; Dementiev, V.; Kutuzov, V. Intelligent data processing methods in sensor networks of mobile and
autonomous objects. IOP Conf. Ser. Earth Environ. Sci. 2021,857, 012001. [CrossRef]
171.
El Hamdani, S.; Benamar, N. Autonomous Traffic Management: Open Issues and New Directions. In Proceedings of the 2018
International Conference on Selected Topics in Mobile and Wireless Networking (MoWNeT), Tangier, Morocco, 20–22 June 2018;
pp. 1–5. [CrossRef]
172.
Axelrod, C.W. Cybersecurity in the age of autonomous vehicles, intelligent traffic controls and pervasive transportation networks.
In Proceedings of the 2017 IEEE Long Island Systems, Applications and Technology Conference (LISAT), Farmingdale, NY, USA,
5 May 2017; pp. 1–6. [CrossRef]
173.
Axelrod, C.W. Cybersecurity challenges of systems-of-systems for fully-autonomous road vehicles. In Proceedings of the 2017
13th International Conference and Expo on Emerging Technologies for a Smarter World (CEWIT), Stony Brook, NY, USA, 7–8
November 2017; pp. 1–6. [CrossRef]
174.
Sharma, P.K.; Vohra, D.; Rathore, S. Security and Privacy in V2X Communications: How Can Collaborative Learning Improve
Cybersecurity? IEEE Netw. 2022,36, 32–39. [CrossRef]
J. Cybersecur. Priv. 2023,3542
175.
Ahmed, M.; Iqbal, R.; Amin, S.; Alhabshneh, O.; Garba, A. Autonomous Vehicle and its Adoption: Challenges, Opportunities,
and Future Implications. In Proceedings of the 2022 International Conference on Emerging Trends in Computing and Engineering
Applications (ETCEA), Karak, Jordan, 23–24 November 2022; pp. 1–6. [CrossRef]
176.
Stinson, M.; Zou, B.; Briones, D.; Manjarrez, A.; Mohammadian, A.K. Vehicle ownership models for a sharing economy with
autonomous vehicle considerations. Transp. Lett. 2021,15, 1–17. [CrossRef]
177.
Taylor, C.R.; Carter, J.M.; Huff, S.; Nafziger, E.; Rios-Torres, J.; Zhang, B.; Turcotte, J. Evaluating Efficiency and Security of
Connected and Autonomous Vehicle Applications. In Proceedings of the 2022 IEEE 19th Annual Consumer Communications &
Networking Conference (CCNC), Las Vegas, NV, USA, 8–11 January 2022; pp. 236–239. [CrossRef]
178.
Boddupalli, S.; Chamarthi, V.S.G.; Lin, C.W.; Ray, S. CAVELIER: Automated Security Evaluation for Connected Autonomous
Vehicle Applications. In Proceedings of the 2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC),
Macau, China, 8–12 October 2022; pp. 4335–4340. [CrossRef]
179.
Kaasen, A.D.; Grov, G.; Mancini, F.; Baksaas, M. Towards data-driven autonomous cyber defence for military unmanned
vehicles-threats & attacks. In Proceedings of the MILCOM 2022-2022 IEEE Military Communications Conference (MILCOM),
Rockville, MD, USA, 28 November 2022–2 December 2022; pp. 861–866. [CrossRef]
180.
He, Q.; Meng, X.; Qu, R.; Xi, R. Machine Learning-Based Detection for Cyber Security Attacks on Connected and Autonomous
Vehicles. Mathematics 2020,8, 1311. [CrossRef]
181.
Security of the Perception in Autonomous Driving under Physical-World Adversarial Attacks. 2022. Available online: https:
//bpb-us-w2.wpmucdn.com/wp.ovptl.uci.edu/dist/e/3/files/2022/10/ICS-1.pdf (accessed on 23 July 2023).
182.
Cui, C.; Du, H.; Jia, Z.; He, Y.; Yang, Y.; Jin, M. Data Poisoning Attack Using Hybrid Particle Swarm Optimization in Connected
and Autonomous Vehicles. In Proceedings of the 2022 IEEE Asia-Pacific Conference on Computer Science and Data Engineering
(CSDE), Gold Coast, Australia, 18–20 December 2022; pp. 1–5. [CrossRef]
183.
Khan, I.A.; Moustafa, N.; Pi, D.; Haider, W.; Li, B.; Jolfaei, A. An Enhanced Multi-Stage Deep Learning Framework for Detecting
Malicious Activities From Autonomous Vehicles. IEEE Trans. Intell. Transp. Syst. 2022,23, 25469–25478. [CrossRef]
184.
Mishra, A.; Cha, J.; Kim, S.; Privacy-preserved in-cabin monitoring system for autonomous vehicles. Comput. Intell. Neurosci.
2022,2022, 5389359. [CrossRef]
185.
Jarouf, A.; Meskin, N.; Al-Kuwari, S.; Shakerpour, M.; Cassanderas, C.G. Security analysis of merging control for connected and
automated vehicles. In Proceedings of the 2022 IEEE Intelligent Vehicles Symposium (IV), Aachen, Germany, 4–9 June 2022;
pp. 1739–1744.
186.
Brubaker, C.; Jana, S.; Ray, B.; Khurshid, S.; Shmatikov, V. Using Frankencerts for Automated Adversarial Testing of Certificate
Validation in SSL/TLS Implementations. In Proceedings of the 2014 IEEE Symposium on Security and Privacy, Berkeley, CA,
USA, 18–21 May 2014; pp. 114–129. [CrossRef]
187.
Kim, J.S. Design of Federated Learning Engagement Method for Autonomous Vehicle Privacy Protection. In Proceedings of
the 2022 Joint 12th International Conference on Soft Computing and Intelligent Systems and 23rd International Symposium on
Advanced Intelligent Systems (SCIS&ISIS), Ise, Japan, 29 November 2022–2 December 2022; pp. 1–2. [CrossRef]
188.
Krichen, M.; Lahami, M.; Al–Haija, Q.A. Formal Methods for the Verification of Smart Contracts: A Review. In Proceedings of
the 2022 15th International Conference on Security of Information and Networks (SIN), Sousse, Tunisia, 11–13 November 2022;
pp. 1–8. [CrossRef]
189.
Abdellatif, T.; Brousmiche, K.L. Formal Verification of Smart Contracts Based on Users and Blockchain Behaviors Models. In
Proceedings of the 2018 9th IFIP International Conference on New Technologies, Mobility and Security (NTMS), Paris, France,
26–28 February 2018; pp. 1–5. [CrossRef]
190.
Cheon, J.H.; Han, K.; Hong, S.M.; Kim, H.J.; Kim, J.; Kim, S.; Seo, H.; Shim, H.; Song, Y. Toward a secure drone system: Flying
with real-time homomorphic authenticated encryption. IEEE Access 2018,6, 24325–24339. [CrossRef]
191.
Sultan, A.; Tahir, S.; Tahir, H.; Anwer, T.; Khan, F.; Rajarajan, M.; Rana, O. A novel image-based homomorphic approach for
preserving the privacy of autonomous vehicles connected to the cloud. IEEE Trans. Intell. Transp. Syst.
2022
,24, 1936–1948.
[CrossRef]
192.
Karras, A.; Karras, C.; Giotopoulos, K.C.; Tsolis, D.; Oikonomou, K.; Sioutas, S. Peer to Peer Federated Learning: Towards Decen-
tralized Machine Learning on Edge Devices. In Proceedings of the 2022 7th South-East Europe Design Automation, Computer
Engineering, Computer Networks and Social Media Conference (SEEDA-CECNSM), Ioannina, Greece, 23–25 September 2022;
pp. 1–9. [CrossRef]
193.
Karras, A.; Karras, C.; Giotopoulos, K.C.; Tsolis, D.; Oikonomou, K.; Sioutas, S. Federated Edge Intelligence and Edge Caching
Mechanisms. Information 2023,14, 414. [CrossRef]
194.
Wang, J.; Cai, Z.; Yu, J. Achieving Personalized
k
-Anonymity-Based Content Privacy for Autonomous Vehicles in CPS. IEEE
Trans. Ind. Inform. 2020,16, 4242–4251. [CrossRef]
195.
Torre, G.D.L.; Rad, P.; Choo, K.K.R. Driverless vehicle security: Challenges and future research opportunities. Future Gener.
Comput. Syst. 2020,108, 1092–1111. [CrossRef]
J. Cybersecur. Priv. 2023,3543
196.
Schizas, N.; Karras, A.; Karras, C.; Sioutas, S. TinyML for Ultra-Low Power AI and Large Scale IoT Deployments: A Systematic
Review. Future Internet 2022,14, 363. [CrossRef]
197.
Antonini, M.; Pincheira, M.; Vecchio, M.; Antonelli, F. An Adaptable and Unsupervised TinyML Anomaly Detection System for
Extreme Industrial Environments. Sensors 2023,23, 2344. [CrossRef] [PubMed]
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