Unmanned Transport Environment Threats

Abstract

Unmanned private and public transport may be susceptible to attacks through various interfaces including networks and physical sensors. With the spread of smart transport and the urban environment that can interact with vehicles, such threats will become increasingly relevant. The paper presents the overview of current cases of attacks on the connected unmanned transport environment that includes smart cars and smart city infrastructure. The paper includes the overview of implementation and classification of such environment components: smart vehicle components and smart city components that can interact with each other. Based on the implementation of components and what technologies they are used, paper overviews attack cases. The attack cases are based on the current reports of security incidents in the past and related research. The paper discusses the most urgent threats for such smart city environment based on the analysis of found attacks and classes of interfaces. The work highlights that today the most serious threat remains the problem of cyber-physical and network security.

Full text

Smart Innovation, Systems and Technologies 187
Andrey Ronzhin
Vladislav Shishlakov Editors
Proceedings of 15th International
Conference on Electromechanics
and Robotics “Zavalishin’s
Readings”
ER(ZR) 2020, Ufa, Russia, 15–18 April
2020

Smart Innovation, Systems and Technologies
Volume 187
Series Editors
Robert J. Howlett, Bournemouth University and KES International,
Shoreham-by-sea, UK
Lakhmi C. Jain, Faculty of Engineering and Information Technology,
Centre for Artificial Intelligence, University of Technology Sydney,
Sydney, NSW, Australia

Editors
Andrey Ronzhin
St. Petersburg Institute for Informatics
and Automation of the Russian
Academy of Sciences
St. Petersburg, Russia
Vladislav Shishlakov
Saint Petersburg State University
of Aerospace Instrumentation
St. Petersburg, Russia
ISSN 2190-3018 ISSN 2190-3026 (electronic)
Smart Innovation, Systems and Technologies
ISBN 978-981-15-5579-4 ISBN 978-981-15-5580-0 (eBook)
https://doi.org/10.1007/978-981-15-5580-0
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Chapter 32
Unmanned Transport Environment
Threats
Maxim Kolomeets , Ksenia Zhernova , and Andrey Chechulin
Abstract Unmanned private and public transport may be susceptible to attacks
through various interfaces including networks and physical sensors. With the spread
of smart transport and the urban environment that can interact with vehicles, such
threats will become increasingly relevant. The paper presents the overview of current
cases of attacks on the connected unmanned transport environment that includes
smart cars and smart city infrastructure. The paper includes the overview of imple-
mentation and classification of such environment components: smart vehicle compo-
nents and smart city components that can interact with each other. Based on the
implementation of components and what technologies they are used, paper overviews
attack cases. The attack cases are based on the current reports of security incidents
in the past and related research. The paper discusses the most urgent threats for such
smart city environment based on the analysis of found attacks and classes of inter-
faces. The work highlights that today the most serious threat remains the problem of
cyber-physical and network security.
32.1 Introduction
In the future, unmanned vehicles will become an integral part of smart cities. Many
large companies are involved in unmanned vehicles development and implementa-
tion. At the same time, there is the development of both types of transport: private
(cars, etc) and public (buses, trams, etc). For cities of particular interest is the possi-
bility of integrating unmanned vehicles with smart city systems. In this case, it
M. Kolomeets (B)·K. Zhernova ·A. Chechulin
St. Petersburg Institute for Informatics and Automation of the Russian Academy of Sciences, 39,
14th Line, 199178 St. Petersburg, Russia
e-mail: kolomeec@comsec.spb.ru
K. Zhernova
e-mail: zhernova@comsec.spb.ru
A. Chechulin
e-mail: chechulin@comsec.spb.ru
© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer
Nature Singapore Pte Ltd. 2021
A. Ronzhin and V. Shishlakov (eds.), Proceedings of 15th International Conference
on Electromechanics and Robotics “Zavalishin’s Readings”, Smart Innovation, Systems
and Technologies 187, https://doi.org/10.1007/978-981-15- 5580-0_32
395

396 M. Kolomeets et al.
becomes possible to exchange information between transport and the systems of
smart city. It turns out the implementation of the concept of “connected cars,” with
the participation of urban infrastructure elements: traffic signs, weather stations,
payment systems and others.
While unmanned vehicles and smart city systems have not become widespread,
it is impossible to talk about existing integration cases. However, such integra-
tion carries security risks. During an attack on a separate unit of transport, other
traffic participants and infrastructure do not experience consequences. However,
in a connected transport environment, an attack on one device affects the entire
environment.
In this paper, we consider the interfaces that make up an unmanned transport
environment of a smart city and what threats they currently have. The novelty of the
paper is the interaction model and classification of unmanned transport environment
components. The contribution of the paper is the defined major threats and their
connection with components of unmanned transport environment.
The paper consists of the following sections: Sect. 32.2 is unmanned vehicle envi-
ronment overview—in this section, we present the interaction model and classes of
components, highlight the components of unmanned transport and smart city that
currently used and that can be attacked. Section 32.3 is unmanned vehicle environ-
ment threats—in this section, we examine existing cases of attacks on individual
elements based on the reports and related research. Section 32.4 is summarized
threat—in this section, we summarize threats of individual components, discuss the
threats found and their relevance. The last section is conclusion—summarizing and
future work plans.
32.2 Unmanned Vehicle Environment Overview
To understand the basic threats for unmanned vehicles environment we consider from
which components it is consist and provide their classification.
The basic components of unmanned vehicle environment are vehicles,infrastruc-
ture of smart city and surroundings.Thevehicles types are cars, busses and trol-
leybuses. The infrastructure includes information signs, toll points, monitoring and
parking systems. The surroundings include weather, passengers, building, roadbed
and other.
In our paper, we do not consider railway, water, air and underground transport,
as they move through the allocated zones, and their impact on the rest of the infras-
tructure is not so significant. We consider only automobile and similar vehicles that
share a single roadbed, as well as the infrastructure for it.
So, the environment interaction model includes interaction between vehicles,
infrastructure and surroundings. There are five types of interfaces:
(1) Surroundings ←→ Vehicle. For example, measurement of distance to car by
another car.

32 Unmanned Transport Environment Threats 397
(2) Surroundings ←→ Infrastructure. For example, measurement of temperature
by weather station.
(3) Vehicle ←→ Infrastructure. For example, transferring the information about
bus location to bus stop by bus.
(4) Vehicle ←→ Vehicle. Connected cars concept.
(5) Infrastructure ←→ Infrastructure. Smart city concept.
In modern unmanned vehicles, the following components of interaction with
passengers and with the environment are used (used in Surroundings ←→ Vehicle
and Vehicle ←→ Infrastructure interfaces).
LiDAR. LiDAR is most commonly used to interact with other unmanned vehicles
and with the environment. LiDAR [62,63] is a laser-based technology that allows one
to obtain three-dimensional environmental information. Based on this information,
the artificial intelligence of pilotless vehicles makes a conclusion about the location
of surrounding objects.
Radars. Radars are used for unmanned vehicles less frequently than LiDAR. This
component emits radio waves and detects reflections of these waves from surrounding
objects. [40,59].
Ultrasonics. Ultrasonics use ultrasonic signals to determine the location of
surrounding objects and the distance between objects and unmanned vehicles [51,
59]. The determination of distance is based on the calculation of the reflection time
from an obstacle.
Vehicle Cameras. Also, for monitoring the environment, cameras with different
viewing angles are used: front cameras, rear cameras, side front, side rear.
[18,59,65].
Also, unmanned vehicles have an interface for interacting with passengers outside
and inside the cabin. These interfaces include a visual, speech, tactile interface, as
well as devices for the interaction of passengers with vehicles.
Displays. The visual interface often includes information displays on which the
route, city map, weather conditions, etc., can be displayed. [35,39,40,55] 3D panels
can also be used that give the user the situation on the road [9–12,61].
In addition to internal displays, the visual interface can also include external
displays on cars [15]. The information that is displayed on them can be used by road
users, pedestrians or autonomous systems.
The speech interface [6,7] is used as a voice assistant in transport, which gives
the user the necessary information, and also responds to voice commands.
Interaction with users [8,33,39,52,56] may include passenger counters, a
terminal for fare payment and passenger information systems. Passengers are counted
using an infrared sensor [52]. Payment for travel can be done using payment termi-
nals that equipped with NFC technology [33,39,52], while the data of the passenger
counter is compared with the number of passengers who paid for travel [52].
Smart city transport infrastructure also includes different smart components for
traffic control and prioritization, taxation and fining, citizen notification and inci-
dent monitoring. Such components include next devices (used in Surroundings ←→
Infrastructure and Vehicle ←→ Infrastructure interfaces).

398 M. Kolomeets et al.
Information signs. Is one of the most spread devices for notification. It can be used
in bus stops for notification of bus arrival time [57]. In highways for dynamical change
of speed limit depending on weather conditions [23,52]. In roads for notifications
about incidents and difficulties on the road [26,38]. In parking lots to show the
number of available lots in the area [26]. The information signs usually include LED
array with radio wave transmitter for receiving of information about the message that
should be shown.
The toll points. The systems for taxation on the toll roads. For example, in Saint-
Petersburg for car identification in the tall highway are used DSRC radio wave [5]: the
driver should install the DSRC transponder on a car windshield to have the ability to
pass the tall point without decreasing of car speed [52]. The cars without transponders
pay more and need to stop at the cash toll point. Another toll points technology based
on contactless car identification includes RFID and NFC [1,27,37].
Traffic Counters. Systems for counting of cars on the road. They can be based
on laser, ultrasonic or radar technologies [52] to count the number of cars that pass
some point on the road (example, Moscow transport system [26]). Another solution
is to use the cameras and image recognition [48,69].
Traffic lights. Traffic lights become smart when used information about traffic
load and incidents for management of traffic flows [26]. The basic concept is to use
traffic lights with counters to manage the flow based on the traffic congestion [69].
CCTV. Cameras are used with machine learning for identification of car incidents
the road [26], car sign recognition for automatic fine and car counter. Usually are
used simple IP–cameras.
Weather station. Weather station is used for collecting of data about weather
conditions that can affect driving difficulty and as a result affect the speed limit [23]
and other traffic management systems [26,45,58]. For example, slippery road due
to rain, ice-crusted road, fog or snowfall.
Barriers and other limiters. They are used to limit the access to some areas, for
example, courtyard, toll parking or toll road. In case of toll roads or parking, they
used in connection with toll systems [27,37]. In case of courtyard can be used the
same contactless technologies, or SMS messaging [29]to get the access, resident can
send the SMS to phone that is connected to barrier (guests to get the access should
ask the resident to do it).
Location systems. Such systems are usually not a part of smart city and based on
the satellites systems (GPS, GLONASS, etc.) [43]. But cities usually have systems
that collect locations of public transport in one place, to share the information about
public transport speed and arrival time [26].
Parking lots. Parking usually used the connection of different systems [26]. For
payment and car identification can be used transponders, contactless cards or cameras
with car sign recognition. For car counting and free lots detection are used radars or
cameras. For driver’s notification about number of available lots are used information
signs.
At the radio level, connected cars communicate with each other both through dedi-
cated short-range communication (DSRC) and via Wi-Fi, LTE, WiMAX (Vehicle
←→ Vehicle interaction) [22,25]. The interaction of connected cars with the smart

32 Unmanned Transport Environment Threats 399
city (Vehicle ←→ Infrastructure) is also provided by Wi-Fi, WiMAX, DSRC and
LTE technologies [20]. In addition, Vehicle ←→ Infrastructure interaction can occur
through millimeter-wave (mmWave) microcellular networks [64]. The following
protocols are used as a gateway between the physical layer and the cloud infras-
tructure for the interaction of connected cars with each other (Vehicle ←→ Vehicle)
MAC, LLC, WME, WSMP, TCP/ IP. The same protocols are used for the interaction
of the machine with the infrastructure (Vehicle ←→ Infrastructure) [13].
Smart city infrastructure used traditional computer networks. For uniting infras-
tructure elements are used Ethernet, WI-FI and mobile networks (3G, 4G and
5G).
The components are represented in Table 32.1, where the first column is the
interface type, the second column is the component, and the third column is the
implementation reference. In next section, we consider attacks on these components.
32.3 Unmanned Vehicle Environment Threats
As we know components of unmanned vehicle environment, we can explore which
attacks can be performed.
Today vehicles components and infrastructure are based on SCADA or IoT
systems. It is obvious that they have the same vulnerabilities than other SCADA and
IoT systems. Also, as components are united into the network, network attacks also
can be performed. So, SCADA\IoT and network attacks are common for all compo-
nents, but there are also some specific attacks. In this section, we consider them based
on the reports of the past security incidents and present them in Table 32.1.
LiDAR. Subject to blinding and different kinds of spoofing [44]. One can dazzle
LiDAR using extraneous laser sources. The distortion of data obtained by LiDAR
is possible when relaying the radiation of LiDAR. You can also create noise and
interference using LiDAR radiation from surrounding unmanned vehicles. When
attacking with spoofing, the same physical environment is used as in other attacks;
however, the purpose of this attack is not to disable the sensor but to deceive the
artificial intelligence of the machine [67].
Radars. Radars in unmanned vehicles are also susceptible to attacks, such as
jamming, spoofing and interference [68]. Jamming is performed by saturating the
receiver with a large number of radio signals. Also, the data received by the radar
can be distorted by relaying the transmitted signals. Using various automotive radars,
you can create interference of the desired level and disrupt the radar signal.
Ultrasonics. Attacks on sonars are aimed at drowning out the sonar signal or
creating overlapping echo signals [67].
Car Cameras. Cameras are also subject to physical attacks, such as blinding. They
are not adequately protected; therefore, they can be damaged or incapacitated by a
sufficiently strong light source [67].
Displays. Attacks on the visual interface of unmanned vehicles can pursue a
variety of purposes, for example, in order to show false information or to disable the

400 M. Kolomeets et al.
Table 32.1 Unmanned transport environment threats
Interface Component Implementation Possible attack Summarized threat
Surroundings ←→ Infrastructure Traffic counting [26,48,52]Cyber-physical Cyber-physical and
network
Weather stations [23,26,58,45]Cyber-physical
CCTV [26,58]Cyber-physical and network [16]
Surroundings ←→ Veh i c l e LiDAR [62,63]Cyber-physical [44,67]Cyber-physical
Radars [40,59]Cyber-physical [68]
Ultrasonics [51,59]Cyber-physical [67]
Vehicle cameras [18,59,65]Cyber-physical [67]
Displays [9,10,11,12,15,35,39,40.55,61] Network
Speech interface [6,7]Cyber-physical [54]
Counting passengers [52]Cyber-physical [30]
Payment devices [8,33,39,52,56]Cyber-physical and Network [19]
Veh i c l e ←→ Infrastructure Toll points [1,5,27,52,37]Cyber-physical and network [24,
36,60,68]
Cyber-physical and
network
Barriers and limiters [27,29,37]Cyber-physical and network [46]
Location systems [26,43]Cyber-physical [31,32,47]
Parking lots [26]Cyber-physical and network
(continued)

32 Unmanned Transport Environment Threats 401
Table 32.1 (continued)
Interface Component Implementation Possible attack Summarized threat
Veh i c l e ←→ Ve h i c l e Connected cars [1,13,19,20,22,25,61,64] Network [2,4,13,17,21,28,49] Network
Infrastructure ←→ Infrastructure Computer networks – Network [66] Network
Information signs [23,26,38,52,57] Network [34,53]
Traffic lights [26,69] Network [41]

402 M. Kolomeets et al.
display. For this, ordinary network attacks such as DDoS or MitM attacks can be
used.
The speech interface can be attacked using speech recordings whose frequencies
are moved to the ultrasonic range [54].
Interaction with users includes many different devices that are subject to a variety
of attacks. For example, a device for counting passengers [52] is based on infrared
radiation, and one can spoof it with using of special materials, for example, metallic
foils [30]. Plastic cards or NFC payment terminals are subject to normal network
attacks [19].
Information signs are a very popular target among attackers. The large collection
of attack cases is presented in [34,53]. The hackers change the messages on the
signs with amusing or offensive messages. As the result, the drivers were distracted
or confused and in one case, attack destabilized traffic.
Toll points are based on contactless technologies that are also susceptible to
attacks. For toll points that are based on DSRC is possible to implement basic attacks
that are reliable to car’s DSRC systems [68]. Systems that are based on NFC tech-
nologies strongly depend on the encryption that is in the system. For example, it
was possible to make a copy of the “St. Petersburg NFC Transport Card”. As the
result, one could get several copies of one paid card [24]. The software for cards
cloning using Android phone was published on GitHub, so anyone could do it [36].
The possibility of card cloning was closed after the intervention of Saint-Petersburg
transport committee. There are also possible general attacks that are not related to
toll systems sensors [60].
As traffic counting is based on the CCTV and laser, ultrasonic or radar
technologies, the attacks are similar to attack on cameras and smart cars sensors.
Smart traffic lights can be attacked indirectly by corruption of connected measure-
ment systems (traffic counters, connected cars network, location systems) or directly
by network attack. In [41], traffic lights where corrupted by simple wireless connec-
tion as vendor did not provide any password authentication or encryption on the
traffic lights wireless communication.
Security of CCTV is well studied, and there are many reports on IP cameras
corruption by network and light jamming. The review of CCTV security threats is
presented in [16].
As weather stations are usually devices with multiple weather sensors, there is
possible to do many physical attacks. For example, it is possible to change wind
speed by hovering drone. The drones can also be used for affecting rain sensors
(wetting the sensors), fog sensors (blocking the laser in case of laser measurements)
and others.
Barriers and limiters work in connection with toll points systems and parking lots,
so they also can be attacked indirectly through connected car identification systems.
For barriers that use SMS or phone calls for authentication can be used SS7 attacks
[46].
Location systems can be jammed [31,32,47] that can cause violations in the public
transport schedule. At the same time, modern traffic systems use multiple location

32 Unmanned Transport Environment Threats 403
sensors that are based on GPS, cell towers triangulation and signals between stops
and vehicles, so such scenario can be unlikely.
Parking lots are also a set of different technologies, such as signs, toll points, car
counters, CCTV and barriers. So, attack on parking lot can be performed as attack
on one of its sensors.
Attacks on the Vehicle ←→ Vehicle interface are mostly network attacks, and
they are well studied. For connected cars using DSRC, Wi-Fi, LTE, WiMAX, DoS
attacks, location tracking and spoofing, as well as malware infection are possible [4,
13,17,21,49]. In addition, network attacks such as Sybil attack (the car is displayed
in several places at once) and Timing attack (receiving a message over the network,
the car does not forward it immediately, but creates a delay) are possible [28].
As smart city networks work using traditional networks, for city infrastructure
are possible the same network attacks (DDoS, MitM, etc.) [66].
The Table 32.1 summarizing found attacks for each component in column “Pos-
sible attack.” We also classify them as Cyber-Physical attacks and Network attacks.
In next section, we discuss the summarized threat that we define for each class of
interface.
32.4 Summarized Threat
The most of attacks that we found relying on incidents and research are network and
cyber-physical attacks. The difficulty is that cyber-physical attacks are complicated
to detect because they do not leave traces in systems as cyberattacks, such as network
and device logs. At the same time, some of them are very simple and do not require
any professionalism if the goal is to jam the sensor. For example, for jamming of
weather station can be used drones, for speech sensors can be used mobile phone,
for LIDAR can be used lasers. At the same time, there are some more complicate
attacks that not jamming sensors but changing their measurements.
In another hand, there are some attacks that are owing to lack of authentication
mechanisms—as it was done for traffic lights in [41] and for payment cards in [24].
Such cases are common in IoT and are general for whole security area, but in case
of unmanned vehicle and smart city infrastructure, they can cause more damage.
Based on the “possible attacks” column and “component” column, we can char-
acterize each interface. We define the summarized threat based on the threats that
we found for individual components.
(1) Surroundings ←→ Infrastructure. In interaction of this type are used compo-
nents with measuring instruments. So, attacks are directed on the corruption
of measurements, and we define threat as cyber-physical. As such measuring
instruments are connected (usually with mobile network, such as in IP cameras),
we also define threat as network.
(2) Surroundings ←→ Vehicle. In interaction of this type are also used components
with measuring instruments for car navigation and interaction with passenger.

404 M. Kolomeets et al.
So, attacks are directed on the corruption of measurements, and we define threat
as cyber-physical. Such measuring instruments are usually connected by CAN
bus [14,42], so we cannot say that major threats are based on network threats
(but they possible [3,50]).
(3) Vehicle ←→ Infrastructure. In interaction of this type are used technolo-
gies for near communication. The most cases of such interaction are vehicle
(toll points, barriers, parking) or infrastructure (location signal) authentica-
tion. Authentication has both threats: cyber-physical threat on the level of
radio/laser/sound/other signal transmission and network threat on the level of
access control data management.
(4) Vehicle ←→ Vehicle. Network threats are major for connected cars while data
transmission.
(5) Infrastructure ←→ Infrastructure. Network threats are major for smart city
infrastructure while data transmission.
So, nowadays, the most actual threat to smart city and unmanned vehicles is cyber-
physical and network attacks. With the development of technology and the advent of
commercial solutions, the trend can shift to networks and IoT security, but for today,
there are not many cases. At the same time, cyber-physical attacks will not become
less actual due the connectivity of components (when attack on one device leads the
changing of measurements in a whole system) and relative ease of use.
32.5 Conclusion
This paper presents the part of ongoing research on security of interfaces that are
used in unmanned transport environment. In paper, we present the classification of
vehicles and infrastructure components that are used in unmanned transport environ-
ment, threats that are based on reports and summarized threat for each class. Such
classification is helpful to specialists who wants to define what security actions need
to be done while development and deployment of unmanned transport infrastructure.
The future work of this investigation is to define intruder model, dependency
graph of components and present risk analysis technique based on them.
Acknowledgements This investigation was funded by RFBR Project №19-29-06099.
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