Assessing the Impact of Attacks on Connected and Autonomous

Vehicles in Vehicular Ad Hoc Networks

Kaushik Krishnan Balaji, Dimah Almani

and Steven Furnell

Cyber Security Research Group, School of Computer Science, University of Nottingham, Nottingham, U.K.

Keywords: Attacks, Blackhole, CAV, DDoS, Grayhole, MITM, VANET.

Abstract: The transportation sector is evolving rapidly towards more sustainable and safer solutions with the idea of

Connected and Autonomous Vehicles (CAVs) based upon Vehicular Ad-hoc Network technology. The

biggest challenge for CAVs is the security threats due to their open nature and internet connections, opening

a wide range of vulnerabilities. In this research, the impact of four cyber security attacks (Distributed Denial

of Service (DDoS), Man-in-the-Middle (MITM), Blackhole and Grayhole) is quantified in terms of network

and transportation performance metrics. The map is setup based on a busy urban area in a UK city, and a

combination of OMNeT++, Sumo and Veins software tools are used for modelling and simulating the attacks

on the network. The simulation is performed with and without the attacks for an accident scenario. MITM is

found to have maximum impact severity on the transportation operational efficiency and safety of the CAV

network. The dynamic rerouting algorithm of the network is identified as the most vulnerable attack vector,

experiencing maximum impact from all the attacks. A maximum packet loss of 82% is achieved by a DDoS

attack. These insights showcased the importance of analysing the impacts of security attacks on the

transportation efficiency of the CAV network, which is vital for building reliable and safer next-generation

mobility systems.

1 INTRODUCTION

Seen as a key element in the future of the

transportation sector, evolving from independent

manually operated vehicles to those that are able to

operate autonomously while communicating with

each other and via the wider Internet. CAVs have the

potential to save billions of people by preventing road

accidents, providing fast emergency services, and

alerting operators of impending hazards. All of these

features are achieved through a dedicated wireless

network that enables the communication between

vehicles (V2V) and infrastructures (V2I). This

communication technology is a Vehicular Ad-hoc

Network (VANET), a decentralized wireless network

capable of disseminating data through a hopping

mechanism. IEEE 802.11p - also known as Dedicated

Short-Range Communication (DSRC) or WAVE - is

the standard that defines the radio frequency channels

and the specifications for V2V, V2I and V2X

communication (Arena et al. 2020). VANET

https://orcid.org/0000-0001-6319-4840

https://orcid.org/0000-0003-0984-7542

technology focuses only on communication between

moving and stationary nodes and infrastructure.

CAVs are advancement over VANET technology

with additional sensors and powerful standalone

computational units capable of decision-making and

autonomous traversal. The CAV network is

connected to the Internet, which makes it prone to

cyber-attacks that exploit the vulnerability of

vehicles. Since these attacks may directly impact

human life, it is crucial to study the impact of these

attacks on CAV to develop a cyber-resilient

transportation network.

Much of the research on cyberattacks is focused

on the network efficiency of VANETs. They study

the impact of attacks on the routing protocols and how

to improve the efficiency of networks in terms of only

packet delivery ratio and data throughput without

considering safety and transportation efficiency.

There is a knowledge gap about how the impact of

attacks on the communication network influence the

traffic behaviour and safety of CAVs. This is vital

774

Balaji, K., Almani, D. and Furnell, S.

Assessing the Impact of Attacks on Connected and Autonomous Vehicles in Vehicular Ad Hoc Networks.

DOI: 10.5220/0011894200003405

In Proceedings of the 9th International Conference on Information Systems Security and Privacy (ICISSP 2023), pages 774-782

ISBN: 978-989-758-624-8; ISSN: 2184-4356

 2023 by SCITEPRESS – Science and Technology Publications, Lda. Under CC license (CC BY-NC-ND 4.0)

because despite development of any level of

prevention mechanisms, the attackers may always a

vulnerability to exploit. This study analyses the

impact of four classes of cyber attacks on the

transportation operations and safety of CAV network,

to provide better understanding of building effective

mitigation systems and plans.

We have investigated and simulated different case

scenarios based on such attacks in VANETs. The map

is set up in a busy urban area in a UK city. A

combination of OMNeT++, Sumo, and Veins

software tools are used for modelling and simulating

the attacks on the network. The simulation is

performed with and without attacks under some

accident scenarios. The analysis is performed with the

base scenario as a benchmark against attacks. The

result is quantified by compare and contrast method.

The main discussion begins with background on

CAV and VANET technologies and the attacks to

which they may be exposed. This is followed by some

discussion of prior works that have considered the

attack scenarios, and the opportunity to extend from

this. Section 4 then describes the approach taken in

this research, including the simulation tools and

parameters, and the attacks to be simulated. The

associated findings are then presented in Section 5,

looking at the impact of attacks under different

simulation conditions. Finally, Section 6 concludes

the discussion and highlights future research

opportunities.

2 BACKGROUND

CAV is an application of VANETs and differs in a

way that all nodes in CAV are considered to be

powerful standalone units with sensors, storage, and

networking capability. In VANETs, the nodes are

considered only as mobile access points. Since CAVs

use VANETs for networking, it is relevant to

investigate where the attacks occur in VANETs.

The VANETs consist of On-board Units (OBUs)

inside the vehicle, Road-Side Unit (RSUs) in the

roads which forms part of the infrastructure, and the

node which is the vehicle itself (Hamida et al. 2015).

These units interact to support Vehicle-to-Vehicle

(V2V) and Vehicle-to-Infrastructure (V2I)

communication. Using 802.11p, the vehicles can

share safety, service and network messages among

other vehicles within the range. Furthermore, the

RSU enables the message transmission to other

vehicles farther from the source vehicle Hence it is

vital to analyse and secure all possible cyber-attacks

which otherwise would lead to loss of life and

property.

Hamida et al. (2015) indicate that V2I

communication links RSU and OBU to Trusted Third

Parties (TTPs) such as service providers, government

authorities (police, emergency) and car

manufacturers providing various services like

entertainment media, software updates over the air, ad

services, etc. It is clear that CAVs will expand into all

forms of life, making the network's security the top

research priority. VANETs are designed to identify

the traffic congestion in the network and the ability to

reroute the vehicles to reduce traffic delay (Milojevic

& Rakocevic, 2013). This study primarily focuses on

the simulation of this dynamic rerouting feature of

VANETs by analysing the traffic delay and other

impacts caused by security attacks.

Cyber attacks targeting the VANET can take

various forms, as categorised and summarised in

Table 1 (noting that those marked with * denote

attacks that are selected for more specific study in the

experimental part of the paper) (Hasrouny et al.,

2017).

Table 1: Forms of attack in VANET contexts.

Attack type Description

Availabilit

Denial of

Service

(DoS)

The network is overloaded by

packet flooding to stop sending or

receiving legitimate messages

between vehicles and

infrastructure. The attack is

initiated b

onl

one vehicle.

Distributed

Denial of

Service

(DDoS)*

Carried out by many attackers in

the network by simultaneously

flooding the network. It is more

difficult to detect and can disrupt

the entire transportation network.

Jamming

Reducing the network capabilities

by creating noisy communication

signals and overloads increasing

the network interference.

Spamming

The network is flooded with

unwanted spam messages that take

up the bandwidth and reduce the

network efficiency by increasing

latency.

Blackhole*

The nodes create fake messages

claiming a short route to

destination and establish a trusted

connection into the network. Once

perpetrated, it deletes the packets

received creating a break in the

chain of route messages leading to

e halts of vehicles.

Assessing the Impact of Attacks on Connected and Autonomous Vehicles in Vehicular Ad Hoc Networks

775

Table 1: Forms of attack in VANET contexts (cont).

Attack type Description

Identification / Authentication

Man in the

Middle*

The attacker enters the network as

an authenticated genuine node and

intercepts the messages to tamper,

delay, spoof and drop in the

network. Since the attacker can

intercept the messages, it provides

the root for all other kinds of

attacks.

Sybil The attacker creates many

identities in the network creating

an illusion of congestion to other

nodes thus creating malfunctioning

of network operations.

Replay The attacker replays an older

authentication message and enters

the network in disguise of an

authorized use

Wormhole A pair of attackers create a

message broadcast tunnel in the

network. Nodes that interact within

this tunnel will falsely think the

other nodes are nearby but not in

realit

Confidentiality/Pri

Eavesdropping The attackers enter the network as

legitimate nodes and gather

sensitive information about other

nodes in the network by

eavesdro

on the messa

es.

Tracking The attacker continuously tracks

the location and direction of a

target node in the network.

Integrity

Spoofing Targeted at compromising the

sensors of the nodes by creating

fake signals to trick them. Sensors

widely targeted are GPS. Once the

sensors are compromised, they

generate false information of the

node and is spread in the network.

Grayhole* Similar to a blackhole attack but

differs in the way it deletes the

messages. The attackers selectively

delete messages from the network

to evade detection.

Non-repudiation

Impersonation The attacker obtains the

authentication details of other

vehicles and uses it to make

legitimate nodes send messages on

its behalf. Since it hides behind

normal node, it is difficult to

detect.

Repudiation

The attacker will use the authentic

user ID of others to perform the

attack making it difficult to prove

the attacker’s real identity.

3 RELATED WORK

While prior studies have considered attack scenarios,

there has yet to be work dedicated to evaluating the

impact on VANET efficiency.

Ahmad et al. (2018a) conducted a MITM attack

simulation on VANETs to determine its impact on the

network behaviour. This work analysed the impact of

varying the volume of nodes participating. The attack

included tampering messages, modification of

transmission time, and deletion. The study concluded

that the attack severely reduced the packet delivery

ratio. Loss of critical safety messages leads to poor

transportation safety.

Grover et al. (2013) conducted a similar

experiment by simulating GPS spoofing attacks on

VANETs to determine its effect on the packet

delivery ratio and vehicle speed. It produced similar

results with poor packet delivery ratio and increased

interference affecting the average speed of nodes

significantly. However, both works focused on the

impact of cyber-attacks created on the VANET

communication protocol operations with less regard

to the safety and traffic behaviour of the network.

Garip et al. (2015) and Ekedebe et al. (2015)

evaluated the impact of security attack on CAVs in

terms of performance metrics of network traffic

operations. Garip et al. simulated a botnet attack to

determine the delay created in the travel time of

nodes. The botnets created fake messages stating the

route is clear while it is very congested in reality.

The work of Garip et al. and Ekedebe et al

provided an idea of how to look at network

performance metrics of transportation behaviour. Our

study extends upon these works to fill the knowledge

gaps on the CAV network's safety and traffic flow

behaviour under different cyber-attacks.

4 METHODOLOGY

To evaluate the impact of the previous attacks on

CAVs, the requirements in the simulation step will be

set as follows:

 Choose a suitable simulation tool that operates

VANET communications.

 Design a working simulation environment of

VANET using CAV technology that supports

V2V, V2I and V2X communications.

 Select security attack scenarios that affect

VANET technology.

 Define a real-world map and user-defined vehicle

count, position, movement, and infrastructure

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 Customize simulation parameters to test

different real-world scenarios seamlessly.

 Output the statistical data for comprehensive

analysis of impact of the simulated attacks.

4.1 Simulation Platform and Tools

OMNeT++, Veins, and SUMO collectively satisfy all

the simulation goals. Objective Modular Network

Testbed (OMNeT++) is an extensible, modular,

component-based C++ simulation library and

framework primarily for building network simulators.

OMNeT++ is designed to simulate large-scale

communication networks. Events are generated at

discrete points in time. The system is simulated by

processing these events, which allows the simulation

of complex systems with high accuracy and realism.

OMNeT++ provides a wide range of features that

make it an ideal tool for network simulation. It

supports network protocols, including Ethernet, IP,

TCP/IP, and UDP/IP. It also provides a graphical user

interface (GUI) for creating and running simulations.

Vehicles in Network Simulation (Veins) is a

framework for modelling vehicular networks. It

creates realistic maps and network communication

protocols and defines how the vehicle or

infrastructure interacts. In addition, custom models of

security attacks are created inside the Veins

framework. The Simulator of Urban Mobility

(SUMO) is a traffic mobility framework used for

creating each vehicle's mobility patterns and routes in

a real-world map (Lopez et al., 2018).

Figure 1 depicts how the various elements work

together. The first step of the simulation is to create a

traffic scenario using SUMO. Map, Number of

Vehicles, Position of RSU, Position of Attacker

Vehicles, Vehicle Routes and Speed are the

parameters modelled using SUMO. The chosen

context is an urban city area close to the authors’

university. A related map was downloaded from

OpenStreetMap and processed to a SUMO-

compatible format (using the JOSM tool to clean the

map and remove unwanted objects, and Gatcom

Sumo to build SUMO model files). The cleaned map

is converted to .net and .poly files using netConvert

and polyConvert tools of SUMO as seen in Figure 2.

The .net and .poly files contain network details of the

map like the latitude, longitude, traffic lights, speed

limit, obstacles etc.

Routes, traffic and SUMO configuration files are

then generated to enable OMNeT++ to run the

simulation with the Veins network framework. To

keep the simulation controlled for analysis purposes,

only one trip with a variable number of cars and a

single RSU is created. Three scenarios with varying

traffic density (10, 25 and 40 cars) are modelled to

determine how this affects network performance.

In the simulation, cars start to generate from the

same location every 500ms until the total count is

reached and they follow the Krauss Car-Following

mobility model (Song et al. 2014). The speed of the

cars is set to follow the speed limits of the map route.

In case of any emergency or accident, VANET

broadcasts safety messages to other nodes and RSUs

to warn of the incident and enable rerouting of other

vehicles to avoid further crashes and traffic delays.

For the purposes of this study, an accident is modelled

halfway along a main road route and the RSU is

positioned near the accident spot. The study focuses

on how the network reacts to the accident and how the

ideal performance is affected during different cyber-

attacks.

Figure 1: Simulation framework.

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A full list of the tools and versions used in the

simulation is presented in Table 2.

Table 2: Summary of tools in the simulation platform.

Tool type Tool Version

Network Simulato

OMNeT++ 5.6.2

Network Mobilit

Veins 5.2

Traffic Generato

SUMO 1.3.1

customisation GatCom Sumo 1.04

OpenStreetMap Edito

JOSM 18543

Operating System Windows 10 10

Figure 2: Final simulation map using SUMO.

4.2 Simulation Parameters

The purpose of the simulation is to quantify the

impact of the attacks on vehicular network

performance. Certain assumptions are made for the

simulation:

 Obstacle detection and avoidance techniques are

not considered. Sensors such as Lidar, Radar,

and Cameras are used for these techniques in the

real world.

 Vehicle mobility is fully autonomous without

intervention from the passengers, even during

an accident or emergency.

 After the accident, the vehicle halts for 40

seconds. Although the real-world delay after an

accident is more, a lesser delay time is chosen

for the simulation to consume less computation

time. However, the assumption has a negligible

effect on the network study as with real-world

scenarios.

 The simulation also assumes a certain volume of

vehicles: in each simulation scenario, traffic

flow and vehicle volumes were kept consistent.

This volume was created arbitrarily to represent

the urban environment at different stages of the

day.

 Vehicles will work perfectly without any delay

or crash when there are no attacks.

The parameters adopted for the simulation are as

summarised in Table 3. When the simulation begins,

the cars start moving in the route generated and send

beacons to RSU once in range. The beacons are

WAVE Short Messages containing information about

direction and identification. At this point, the beacons

are just like “hello” to RSU which keeps a record of

the interaction of cars. When the first car meets with

an accident, it broadcasts safety-critical control

messages to RSU and other cars within range. It

identifies alternate routes and broadcasts the route

road ID. Cars outside the range of the accident car

will not receive the alternate route message. RSU will

inform those vehicles once in range. After a delay of

40 seconds, the cars start moving into the alternate

route. Without any attacks, all cars reach their

destination before the simulation time of 240 seconds.

Table 3: Simulation parameters.

Paramete

Setting(s)

Routing Protocol DSRC, WAVE

PHY IEEE 802.11

Mode Wireless

Communication Ran

e 600m

Packet size 132 bytes

RSU 1

Vehicles 10, 25, 40

route s

Threats MITM, DDoS,

Blackhole, Grayhole

Runtime 240s

Accident count 1

Attackers Variable %

As indicated in Table 1, four classes of attack

were selected for study. These are then simulated at

25%, 50% and 75% of attacker cars in the network, to

evaluate the network impact as attacks intensify. The

nature of each attack is described below, along with

associated pseudo-code in Table 4.

 Man-in-the-Middle: Messages received from

normal cars during the accident contain alternate

route data. If tampered with (as assumed here),

rerouting is affected; traffic delays will increase

and may cause more accidents. In our study, the

randomly chosen attacker nodes check for

accident messages from normal cars. Once

detected, the alternate route ID in the message is

replaced with its current route ID and forwarded

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with random delay. When other nodes receive

this message, they will not know about the

accident or alternate route and will drive in the

predefined route leading to more traffic, delay

or crash.

 Blackhole: Attacker cars delete messages

received from normal cars. Receiver cars will

not be aware of the accident and the alternate

route thus creating traffic and other issues.

 Grayhole: Randomly selects the messages it

wants to delete so that it will not be detected by

the network.

 Distributed Denial of Service: Attacker nodes

for DDoS attacks are created separately as

‘Attacker’ cars instead of choosing randomly

from normal cars. Since the attack is flooding

the network with a large volume of packets, the

simulation computation time is too long if the

attacker is chosen randomly. As such, a fixed

number of 4 attackers are created and positioned

at the accident zone for the attack simulation.

Increasing the percentages of attacker cars as

seen in other attacks is not a computationally

viable solution. Instead, the attacker volume is

controlled via a flooding time window. The

attack will only happen within this window. For

example, 25% of attackers create an attack

window of 60 seconds which is 25% of total

simulation time. This window always starts at

the same time as the accident event. The 4

attacker cars then flood the network with 1000

packets of each received message until the

window ends. The packet rate is not pushed to

the network bandwidth limit as it crashes the

simulation software. However, the flooding rate

is sufficient to cause disruption.

All the attacks were simulated with three different

sets of cars (10, 25, 40) and three different attacker

percentages (25%, 50%, 75%) creating a total of 9

scenarios for each attack. After analysis it is found

that the attacker percentages did not have any impact

on DDoS, Blackhole and Grayhole attacks due to the

smaller simulated network. They showed drastic

impact when run for 1000 nodes with an extended

simulation time of 500s. However, due to the scope

and timeline of the research project, heavy network

simulation could not be performed as it required

Table 4: Adversary models for the four simulated attacks.

MITM attac

Blackhole attac

Grayhole attac

DDoS attac

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significant computation time and processing leading

to frequent crashes of the system. The intended

research outcome can be inferred from a smaller

simulation network with negligible inaccuracy. For

the purposes of the results reported here all scenarios

were simulated with different numbers of vehicles

(10, 25, 50) and a fixed proportion of 25% attackers.

The latter is in line with the proportion used by other

studies, such as Ahmad et al. (2018a, 2018b).

5 RESULTS

The main results are depicted in Figures 3 to 7,

contrasting the impacts of different attacks against

simulation scenarios involving different numbers of

vehicles. Figure 3 illustrates the average simulation

time and indicates (as expected) that any of the attack

scenarios serve to increase this over the time taken to

run the base simulations of the attack-free scenarios.

When looking at overall performance, it is evident

that the attacks have a considerable impact on the

travel time of vehicles (see Figure 4). All of the

attacks have delayed the time taken by vehicles to

reach the destination when compared to the base

scenario. DDoS and MITM attacks have similar

impact levels, whereas the travel delay caused by

Blackhole and Grayhole attack is high in all three

scenarios. Hence the network suffers maximum delay

due to Blackhole and Grayhole attacks.

The number of cars also influences the delay,

which is inversely proportional to the vehicle density

in the network. When the number of vehicles

increases, the time it takes to reach the destination

decreases. This is the advantage of the CAVs network

over the traditional transportation system. In the real

world, when the number of cars increases, it leads to

congestion. However, the increased number of nodes

increases the possibility of messages being delivered

to receivers, increasing dynamic routing efficiency.

Packet loss affects the efficiency of the network

(see Figure 5). The packet loss was not significantly

affected at 10 cars compared to 25 and 40 car

networks. The node density is directly proportional to

the packet loss ratio during security attacks. The

DDoS attack has produced maximum damage to the

network, with almost 30% more loss than all the other

attacks. The attack was targeted to crash the network

affecting the availability of resources, which is

evident from these results. Hence, DDoS gets a high

impact score and severely damages network

efficiency. The patterns of other attacks fluctuate for

each scenario, with similar percentages of packet loss.

Unsurprisingly, DDoS was also the most

impactful attack in causing network latency (see

Figure 6). The busy network time for MITM,

Blackhole and Grayhole is almost the same as the

base scenario. Therefore, these attacks do not target

the network's resources, so their impact here is low.

All the cars reached the destination in the base

scenario, so the number of unfinished cars count

value here is 0 and is therefore not visible in the graph

(Figure 7). Meanwhile, the unfinished cars count

during the attacks is directly proportional to the

number of cars in the network. It steadily increases

from 10 cars to 40 cars. Blackhole and Grayhole

achieved 100% results with maximum impact on the

network. However, this seems unrealistic for a real

life outcome and hence these results are set aside as a

limitations of the simulation software.

Figure 3: Average Simulation Time (seconds).

Figure 4: Average Distance Travelled (metres).

Figure 5: Packet Loss Ratio (%).

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Figure 6: Network Busy Time (seconds).

Figure 7: Unfinished Cars Count.

The unfinished cars determine the overall

efficiency of the network in terms of transportation

and safety. Hence it can be considered to have direct

relationship with efficiency of the CAV network. It

can be observed that the MITM attack has more

unfinished cars than DDoS, and so concluded that

MITM is more severe and dangerous for CAVs.

Table 5 summarises the impacts of the different

attack types on the overall operation of the CAV

network.

Table 5: Summary of impacts from different attacks.

DDoS MITM

Black

hole

Gray

hole

Transportation Dela

Low Me

High High

Packet Loss Ratio Hi

hMe

Network Latenc

h Low Low Low

Unfinished Vehicles Hi

hHi

h N/A N/A

MITM attack has the maximum impact on the

safety and transportation efficiency as it disrupts the

rerouting algorithm with tampered messages. The

more MITM attackers the more processing delay of

the network in dynamic rerouting. The risk level of

MITM is high as it directly affects safety. DDoS is

medium as it only affects the emergency services due

to unavailability of the network whereas MITM leads

to further collisions and long halts. Blackhole is low

as it can be easily detected. Grayhole is medium due

to its evasive nature. The number of attackers does

not influence the impact inflicted by DDoS,

Blackhole and Grayhole attack as they all produced

identical results. It only influenced the impact for

MITM attack. DDoS attack consumes maximum

network resources, where the usage for other attacks

remained on par with the base scenario. DDoS also

produced the maximum packet loss. Finally, the CAV

network suffers maximum transportation delay due to

Blackhole and Grayhole attacks

6 CONCLUSIONS

The success of CAVs clearly depends upon trust in

the technology. This will only build based on the

safety and reliability of the transportation services

provided. Hence it is important to focus on building

systems that are resilient to attacks that seek to exploit

the network and communications infrastructure.

This study is a steppingstone for safer future

transportation. Four types of attacks were modeled,

and their impact quantified for safety, network and

transportation efficiency. MITM is identified as the

most severe attack with high risk of affecting the

safety operations. More focus should be given to the

network rerouting algorithm, which is identified as

the most vulnerable feature of CAV by the attacks.

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