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Cybersecurity Readiness for Automated Vehicles
Shah Khalid Khan*1, Nirajan Shiwakoti1, Peter Stasinopoulos1, Matthew Warren2
1School of Engineering, RMIT University Melbourne, Australia
2RMIT Centre for Cyber Security Research and Innovation, RMIT University,
Melbourne, Australia
Email for correspondence: s3680269@student.rmit.edu.au; shahkhalid_k@yahoo.com
Abstract. Autonomous Vehicle (AV) is a rapidly
evolving mobility technology with the potential to
drastically alter the future of transportation. Despite the
plethora of potential benefits that have prompted their
eventual introduction, AVs may also be a source of
unprecedented disruption for future travel eco-systems
due to their vulnerability to cyber-threats. In this
context, this work assesses AVs' cybersecurity
readiness. It establishes a Causal Loop Diagram (CLD)
based on the System Dynamics approach: a powerful
technique inferred from system theory, which can
synthesise the behaviour of complicated AV systems.
Based on the CLD model, three feedback loops and a
system archetype "Fixes-That-Fail" are envisioned, in
which the growth in hacker capability, an unforeseen
result of technology innovation, demands constant
mitigation efforts. The most challenging aspect of this
context is determining the trade-off between five
components: i) the natural growth of AV technology; ii)
stakeholders (communication service providers, road
operators, automakers, AV consumers, repairers, and
the general public) access to AV technology; iii) the
measures to limit hackers' access to AV technology; iv)
a pervasive dynamic strategy for circumventing hacker
amplification; and v) the efficient usage of AV
operating logfiles.
Keywords: Driverless cars, Risk assessment, Cyber-
Physical System, Privacy, Cyber-attack, Safety,
Ransomware.
1. Introduction
Automated Vehicles (AVs) will breathe new life into road transportation.
It has the potential to fundamentally alter how people and goods are
transported in the future. The developments in AV's connectivity, the
integration of sensors (gyroscopic, RADAR, LASER, GPS, ultrasonic,
TPMS, LiDAR, and cameras), the creation of 3D holographic displays, and
virtual reality are the primary enablers of this technology. The most
important benefit is that human error, which causes 94% of all car accidents,
is eliminated.
Dataflow is a key enabler for the operation of AV technology. With at
least 200 sensors, AVs would produce roughly 25 gigabytes of data each
hour, which can generate $450-750 billion in global revenue by 2030 in
predictive maintenance, pay-as-you-drive services, and creative
infrastructure [1]. Despite the plethora of potential benefits that have
compelled their eventual introduction, AVs may also be a source of
unprecedented disruption for tomorrow's travel eco-systems due to their
susceptibility to cyber-threats and hacking [2]. There is no universally
accepted definition or classification of what constitutes a cybersecurity
breach, and there is no comprehensive list of potential hazards. Very
sophisticated cybersecurity assaults often have no end. The aim is to guard
AVs against all possible cyber-attacks, which might result in disruption of
road traffic, consumer/infrastructure damage, and protecting consumers'
sensitive information.
In the event of a successful cyber-attack, the aforementioned disruption
would be amplified. The term "modern-day pirates" has been applied to
cyber-attackers. The massive financial flow ($450-750 billion in global
revenue by 2030) encourages hackers to pursue alternative revenue streams
in addition to ransomware in the AVs operation. Additionally, global
geoeconomics divisions have exacerbated the hacking industry's transition
to "Cyber-attacks-as-a-Service." As a result, establishing the culpability of
AVs hackers operating within a dynamically linked AV-based road
operation is critical.
1.1 Rationale of the study
Academics and industry alike are concerned about the safety and security
of AVs on public roadways. Numerous aspects of AV's cybersecurity have
been examined. For example, there is a focus on AV design that
incorporates technological innovation as well as data transmissions
encryption techniques such as layering, patching, and backup [3]. Several
researchers investigated AVs' infrastructure, focusing on security evaluation
in Vehicle-to-Everything Communications (V2X-C) networks that integrate
5G and 6G cellular networks [4]. The authors [2] synthesised and
interpreted critical areas for the roll-out and progression of CAVs in
combating cyber-attacks. A holistic view of potentially critical avenues,
which lie at the heart of CAV cybersecurity research, is described in a
structured way. CAVs communication framework is presented in an
integrated form, i.e., from In-Vehicle (IV) communication to Vehicle-to-
Vehicle (V2X) communication, with a visual flowchart to provide a
transparent picture of all the interfaces for potential cyber-attacks.
Furthermore, there is a focus on a robust CAVs cyber-policy framework, the
importance of CAVs stakeholders' awareness and understanding
stakeholders, and the relevance of trust in boosting CAVs cybersecurity
initiatives stakeholders [5].
These studies, however, have concentrated on linear interaction and do not
synthesise the complexity inherent in AV deployments. In a traditional
"arms versus armour" technology race, it is projected that Artificial
Intelligence (AI) would enable attacks to expand at the same pace as AI-
enabled defences. Because hackers' expertise and resources are improving,
the concept of acceptable or successful cybersecurity is constantly evolving
[6]. Additionally, it has been shown that machine learning and deep learning
techniques are susceptible to well-designed adversarial perturbations, and
various physical world attacks on the visual systems of AVs have been
successfully carried out. Thus, concurrent research is required to weigh the
benefits and drawbacks of AV technological innovation, i.e., boosting
cybersecurity vs. increasing hackers' capacity during the operation of AVs.
1.2 System Dynamic based Causal Loop Diagram
The Causal Loop Diagram (CLD)-based System Dynamic (SD) approach
is a subset of system theory, which can synthesise the behaviour of
complicated AV systems. CLDs are evaluated in terms of behavioural
patterns, necessitating further knowledge and comprehension [7, 8].
Established in the early 1960s, SD methods provide a firm theoretical
framework for explaining how complex systems function [8]. It has been
applied to the examination and evaluation of a wide variety of dynamic and
complex systems, including those relating to information security [9]. The
authors [10] developed a conceptual SD model to analyse cybersecurity in
the complex, uncertain deployment of CAVs. Specifically, the SD model
integrates six critical avenues and maps their respective parameters that
either trigger or mitigate cyber-attacks in the operation of CAVs using a
systematic theoretical approach. These six avenues are: i) CAVs
communication framework, ii) secured physical access, iii) human factors,
iv) CAVs penetration, v) regulatory laws and policy framework, and iv)
trust—across the CAVs-industry and among the public.
Similarly, the authors [5] developed a CLD-based SD model that
incorporates critical inter-disciplinary parameters and dynamically evaluates
the impact of regulations on CAVs cybersecurity. Two loops are envisioned:
"balancing loops" demonstrate how regulations can facilitate cyber-attacks
prevention, whereas "reinforcing loops" reveal how imposing regulations
can negate its potential benefits by creating a detrimental parallel circle.
Based on the feedback loops, a "shifting the burden" system archetype is
postulated in which governments combat cyber-threats by strengthening
regulations while also reducing CAVs adaptation through imitation and
induction.
Moreover, [11] developed an SD model for strategic cybersecurity
assessment in the CAVs roll-out. The SD model incorporates a Stock-and-
Flow Model, which can integrate multiple perspectives into a single model
and map various parameters that either stimulate or prevent cyber-attacks by
integrating the critical elements of CAV cybersecurity. Due to the fact that
AV technology is constantly evolving and there is a dearth of actual data on
how AVs operate, CLD is an appropriate research strategy for developing a
unified suite of high-leverage technologies and laws to safeguard AVs.
1.3 Contribution of the study
We conducted an integrated dynamic evaluation of the Technological
Maturity (TM) on cybersecurity as well the hacker's capability of AVs. The
key contributions are listed below:
➢ We introduced the SD-based CLD method, a powerful technique
derived from system theory and capable of synthesising and
comprehending the behaviour of complex AVs systems.
➢ We developed the SD model that incorporates key inter-disciplinary
factors pertinent to the technological maturity in AVs cybersecurity
and hacker's ability.
➢ Three feedback loops are described using the CLD model: the
Balancing Loop (BL) #1 shows how TM improves resistance to
cyberattacks; the Reinforcing Loop (RL) #1 shows how TM helps
hackers become more skilled; and the Reinforcing Loop (RL) #2
shows how successful cyberattacks and AV data files allow
malicious actors to "weaponise."
➢ The three feedback loops define the system archetype "Fixes-that-
Fail," emphasising the importance of concurrent actions to thwart
hackers' growth and maximise the benefit of TM in AVs
cybersecurity.
The remainder of the paper is structured as follows. The next section
outlines the methodology adopted. Section 3 elaborates on the conceptual
CLD-based SD Model. The following section describes the feedback loops
and system archetype. Section 5 focuses on discussion and policy
recommendations. The limitations and future extensions of the study are
finally presented in Section 6.
2. Methodology
To investigate the complex, interconnected, and uncertain impact of TM
on AVs cybersecurity, we used a CLD-based SD approach, a technique that
has the potential to investigate the system-level cybersecurity implications
of self-driving cars [8, 12]. CLD visualises model composition using
intuitive graphical diagrams, determines key factors, generates feedback
loops, and identifies a system archetype. The use of system archetypes
enables the efficient improvement of systems. System archetypes may be
used as a diagnostic tool to identify behavioural patterns that have
developed in an undesirable situation. CLD is used to assess the security of
SAE 4 (or higher) self-driving vehicles through the lens of a functional
pathway [13].
3. Proposed structure of the Causal Loop Diagram
The model variables and their mapping are based on solid innovation
theory, i.e., meta-exploratory quantitative analysis of post-2010 literature
derived from various sources, including peer-reviewed journal databases,
books, doctoral dissertations, and credible company surveys, augmented
with forward and backward snowballing. This leads to identifying critical
avenues that are crucial to assessing the impact of TM on AVs cyber-safety
research. The next section describes each parameter's scope, significance,
and influence in the model, as well as giving references.
Additional aspects such as regulations, human factors, and trust are
outside the scope of this study. This choice is mainly motivated by the need
for a restricted border when assessing limited parameters as well as the non-
geographical character of the SD model, although these features are
essential for future research.
3.1 The Causal Loop Diagram (CLD) development
The CLD consists of nodes and edges. Nodes represent the variables, and
edges represent the relationships between the variables. In a positive causal
relationship, both nodes increase in the same direction. In a negative
connection, on the other hand, when one node expands, the other shrinks,
meaning that the two variables move in opposing directions. The two closed
cycles, reinforcing and balancing, are essential features of CLDs.
Reinforcing loop: change in one direction is compounded by additional
change, and balancing loop: change in one direction is countered by a
change in the opposite direction. The arrow with two small lines indicates
the presence of a delay sign. The loops come together to form a system
archetype.
The proposed architecture of the CLD for TM in the AVs cybersecurity is
depicted in Figure 1. The scope of the various variables covered in CLD
(Figure 1) is detailed in a tabular explanation in Table 1, along with
references. The link between dependent and independent variables is shown
in Table 1 in terms of cause and effect. Additionally, it describes the process
of possible effect, its polarity (positive or negative influence), and visualises
the uncertainty. Because there is a dearth of empirical evidence, the
uncertainty rating in Table 1 is based on the synthesis of available literature
and logical conjecture.
Table 1: Factors influencing TM in AVs cybersecurity in the CLD.
Independent Variable
Dependent
Variable
Impact as Process
Uncertainty
Polarity
Sources
Technology Maturity
(TM)
V2X
communication
cybersecurity
TM, which assesses the
communication framework
of CAVs and demonstrates
its capabilities, is triggered
by the level of technology,
procedures, qualified
personnel and information.
Defence Science and
Technology Group in
Australia spotlighted the
nine-level of estimating the
maturity of technologies
during the acquisition phase
of a program. Innovation in
technology maturity will
lead to more secured V2X
communication.
Medium
+
[14, 15].
V2X communication
cyber-security
Resilience to
cyber-attacks
CAVs communication is the
primary interface for cyber-
attacks. A potential breach
of V2X communication
security reduces robustness;
results in the incorrect
location, incorrect speed
value, and incorrect route
adaptation to full CAVs
communication blockage.
High
+
[16-19].
Resilience to cyber-
attacks
AVs
Communication
Cyber Safety
A highly robust CAVs
communication network is
less vulnerable to attacks
and is incredibly difficult for
hackers to infiltrate. A
network more robust to
attacks will enhance the
CAVs cybersecurity, given
their hybrid nature of
attacks, i.e. cyber and
physical.
High
+
[20, 21]
AVs Communication
Cyber Safety
Probability of
Hacks
Defended
A highly robust CAVs
communication framework
is less vulnerable to attacks
and is incredibly difficult for
hackers to infiltrate.
Additionally, Unnamed
Aerial Vehicles may
function as an ad-hoc, cost-
High
+
[22-28]
effective
telecommunications
network, allowing CAVs to
communicate in
mountainous or dark
regions. For instance, the
use of UAVs in conjunction
with 5G networks, both for
access and backhaul,
Probability of Hacks
Defended
Successful
Cyber-Attacks
The high number of hacks
being defended will
minimise the number of
successful CAVs cyber-
attacks.
High
-
Successful cyber-attacks
Log Files
Preservation
CAV's network
observability is primarily
based on log files. All CAV
activities in ITS are
documented in log files.
Cyber-assaults-valuable
input to log file preservation
-can serve as lessons learned
and aid in investigating
hacker attacks and
motivations.
Medium
+
[29]
Log Files Preservation
Technology
Maturity
Retaining log files for all
CAVs interactions for a
specified period would
improve ITS reliability,
protect CAVs cybersecurity
posture of cloud computing
environments, and enhance
CAVs decision-making
Medium
+
[30]
Log files preservation
The hacker's
ability
Preservation of log files over
a specified period for all
CAVs interactions, i.e.,
physical and communication
data file with stringent
privilege access. The
rationale is threefold; i) data
files are useful input as
information to TRM, ii)
enhance cybersecurity
lessons learned could
facilitate the handling and
investigation of hacker
attacks and motives, iii)
robust evidence in assessing
insurance liabilities.
However, the CAVs log file
protection is also a potential
risk, i.e., sweet data for the
hacktivists.
High
+
[29-31]
Figure 1 : The CLD system architecture.
4. Model Qualitative Analysis: Loops and System archetype.
CLDs conceptualise dynamic systems coherently in order to enable
comprehension of the interdependence of TM in the cybersecurity context of
AVs. The loops propose a "system archetype" that reveals the system's
underlying constraints by indicating intervention possibilities, enabling the
development of relevant policy proposals [8]. The following sub-sections
describe various feedback loops and the envisoned system archetype.
BL#1: Figure 2a illustrates BL #1, in which TM amplifies AVs-CS,
increases Resilience to Attacks owing to advancements in V2X security.
Similarly, it illustrates how the technological innovation would be able to
withstand cyber-attacks due to the higher Probability of Hacks Defended.
RL #1: Figure 2b shows RL#1, in which Log-Files-Preservation strengthen
TM. The increase in TM enables the Hacker's ability to lift the Successful-
cyber-attacks. However, the attacks provide a valuable source of log files
due to the lesson learned.
RL#2: Figure 2c depicts the process described in RL #2, in which
Successful-cyber-attacks allow malicious actors to "weaponise." Moreover,
the experience and knowledge obtained throughout the battle on the design
of AVs and cloud computing dataflow might be used to attack any future
flaws. Additionally, the log file records all AVs operations in road
transportation: from user behaviour to communication architecture and is a
goldmine for hackers.
4.5 Holistic View: Fixes-that-Fail system archetype
The loops envision a system archetype that elucidates underlying system
constraints by emphasising intervention opportunities. Figure 3 depicts a
holistic view and is related to the "Fixes-that-Fail" archetype: which is used
to express and analyse instances where a remedy has unanticipated long-
term effects on the system's behaviour, demanding further solutions.
In terms of AVs cybersecurity, it refers to the technique by which the
industry mitigates the perceived cybersecurity danger posed by hackers by
increasing the resilience of AVs communication to assaults, thereby
lessening successful attacks, referred to as BL#1. However, hackers,
unintentionally, gain from advanced technology as well. Hackers employ
technology to augment their powers, most often to the point where the
perceived
a. Balancing loop #1
danger is completely restored. Thus, the reinforcement cycle, shown by
RL#1 and RL#2, continues. Increased hacker capability is an unintended
result of TM, demanding further mitigation measures. Breaking the pattern
of "Fixes-that-fail" demands an understanding that mending just alleviates a
system's symptoms and a commitment to tackle the underlying issue.
a. Reinforcing loop #1
b. Reinforcing Loop #2
Figure 2: Feedback loops in CLD
Figure 3: Holistic view- "Fixes-that-Fail" system archetype
5. Discussions and policy recommendations
This paper aims to incorporate critical cross-disciplinary parameters that
dynamically assess the impact of TM on AVs' cybersecurity vs. hacker
ability. Recognising common structures, referred to as "system archetypes,"
is an important first step in the long-term deployment of AVs that is secure
and reliable. The core premise of system archetypes is that adverse
outcomes or side effects may be associated with common behavioural
patterns. The creation of a CLD-based SD model for evaluating the
influence of TM on the cybersecurity of AVs is crucial because it gives a
dynamic, linked perspective of the "whole picture."
The "Fixes-that-Fail" pattern illustrates how to cope with a hacker's ability
and elucidates the never-ending conflict between defenders and attackers in
AVs' cybersecurity. The hacking world is highly diversified, ranging from
hacktivists to individual hackers, as well as the participation of well-
established non-geographical organisations. Additionally, hacking is far
more attractive in the vulnerable Internet-on-Wheel environment (such as
AVs). Consequently, in addition to protecting the fundamentals of AV
technology, design, parameters, and driving models from the adversary, it is
critical to developing a systematic plan for defeating the AV hacker threat in
both the cyber and physical realms.
The most challenging aspect of this context is determining the trade-off
between five aspects: i) the natural growth of AV technology; ii)
stakeholders (communication service providers, road operators, automakers,
AV consumers, repairers, and the general public) access to AV technology;
iii) the measures to limit hackers' access to AV technology; iv) a pervasive
dynamic strategy for circumventing hackers' amplification; and v) the
efficient usage of AV operating logfiles [32].
6. Limitations and future extensions
While the proposed paradigm is comprehensive, thorough, and rigour-
filled, it does have certain limitations. The model's empirical evaluation is
complicated by data scarcity, a high degree of uncertainty, and the
subjectivity inherent in TM. As a result, the coming phases may include
data gathering in order to conduct a quantitative assessment of the model.
The main source will be a survey of qualified subject professionals,
supplemented by pilot programmes like Australia's "Austroads Future
Vehicles & Technology Program" [33]. On the other hand, qualitative
research may still be the most popular choice for a few more years, until the
right amount of data is available.
7. Conclusions
This paper proposes a CLD-based SD model that incorporates key inter-
disciplinary variables and evaluates the impact of TM on AVs' cybersecurity
in a dynamic and integrated manner. The CLD model illustrate three
feedback loops: BL#1 shows how TM improves resistance to cyberattacks;
the RL#1 describes how TM helps hackers become more skilled; and the
RL#2 demonstrates how successful cyberattacks and AV data files allow
malicious actors to "weaponise." Based on three feedback loops a system
archetype, "Fixes-that-Fail" is envisioned, in which the growth in hacker
capability, an unintended result of technology maturity, demands constant
mitigation efforts. The method used is believed to open up new avenues for
the research community's methodical exploration of complex systems like
AVs, while the recommendation serves as a first-order analysis for AVs
cybersecurity.
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