Evaluating the Dedicated Short-range Communication for Connected

Vehicles against Network Security Attacks

Tu Le, Ingy Elsayed-Aly, Weizhao Jin, Seunghan Ryu, Guy Verrier, Tamjid Al Rahat, B. Brian Park

and Yuan Tian

School of Engineering and Applied Science, University of Virginia, Charlottesville, Virginia, U.S.A.

Keywords:

Cybersecurity, Network, DSRC, Connected Vehicles, VANET.

Abstract:

According to the National Highway Trafﬁc Safety Administration, there are more than 5 million road crashes

every year in the U.S. More than 90 people die in car crashes every day. Even though the number of people sur-

viving crashes has increased signiﬁcantly thanks to safety features, such as airbags and anti-lock brakes, many

people experience permanent injuries. The U.S. Department of Transportation introduced connected vehicle

technologies, which enables vehicles to “talk” to each other and exchange important data on the roads, with

the goal of preventing crashes from happening in the ﬁrst place. With the rapid development of autonomous

driving technology, vehicles in the near future will be able to operate completely without human drivers,

increasing the need of reliable connected vehicle technologies. Due to the safety-critical characteristics of

autonomous vehicles, it is important to evaluate the technologies extensively prior to deployment to ensure the

safety of drivers, passengers, and pedestrians. In this paper, we evaluate the safety of Dedicated Short-Range

Communication (DSRC), which is a popular low-latency wireless communication technology speciﬁcally de-

signed for connected vehicles. We present three real-world network security attacks and conduct experiments

on real DSRC-supported modules. Our results show that DSRC is vulnerable to these dangerous attacks and

such attacks can be easily implemented by adversaries without signiﬁcant resources. Based on our evaluation,

we also discuss potential countermeasures to better improve the security and safety of DSRC and connected

vehicles.

1 INTRODUCTION

With utilization of cutting-edge technologies in trans-

portation, communication, and control, roadways are

becoming connected. The connections among vehi-

cles and infrastructure enable the urban transportation

system to play a vital role in addressing mobility and

sustainability concerns. Those connections have been

introduced as vehicle-to-vehicle (V2V) and vehicle-

to-infrastructure (V2I) communication. In addition,

smart vehicle’s features such as lane departure warn-

ing, obstacle avoidance, and autonomous driving sup-

port the role of the urban transportation system to im-

prove its performance.

Autonomous driving contains ﬁve levels based

on their degree of autonomy. Higher level of

autonomous driving requires less human involve-

ment (BMW, 2019; NHTSA, 2020). For example,

level 5 represents fully autonomous without human

controls (NHTSA, 2020). The automated driving sys-

tem overcomes distractions of human drivers; how-

ever, communication security becomes more impor-

tant. As drivers’ control will decrease along with the

level of autonomy, the automated driving system will

heavily rely on communication system.

Dedicated Short-Range Communication (DSRC)

has been adopted in smart vehicles as the most popu-

lar communication protocol. It is a low-latency wire-

less communication architecture for node-to-node

communication among hardware-enabled vehicles

and roadside equipment. DSRC includes Road Side

Units (RSUs; roadside infrastructure) and On-Board

Units (OBUs; travelling vehicles) with transceivers

and transponders. DSRC over different radio spec-

trum bands is already being used in North America,

Europe, and Japan for several applications such as

electronic toll collection (Abboud et al., 2016).

It is expected that DSRC will be more widely used

in the smart vehicles due to its growing popularity.

However, it is important to carefully investigate the

DSRC because one of the greatest security threats to

future automotive systems is DSRC itself. Therefore,

this paper studies the security of DSRC communica-

tion and seeks to determine how secure the protocol

Le, T., Elsayed-Aly, I., Jin, W., Ryu, S., Verrier, G., Al Rahat, T., Park, B. and Tian, Y.

Evaluating the Dedicated Short-range Communication for Connected Vehicles against Network Security Attacks.

DOI: 10.5220/0009355500370044

In Proceedings of the 6th International Conference on Vehicle Technology and Intelligent Transport Systems (VEHITS 2020), pages 37-44

ISBN: 978-989-758-419-0

is against network security attacks in practice.

In this paper, our main contributions are summa-

rized as follows:

• We present three potential security attacks target-

ing the V2V and V2I communications: commu-

nication jamming, man-in-the-middle forwarding,

and false alert.

• We evaluate the robustness of the DSRC protocol

for connected vehicles against these attacks.

• We introduce some countermeasures to better pro-

tect DSRC against these network security attacks.

The remainder of this paper is organized as fol-

lows. Section 2 describes our literature review of re-

lated research. Section 3 presents the threat model

and the network security attacks we implement to

evaluate the DSRC protocol. Section 4 reports

our evaluation of the proposed attacks on DSRC-

supported devices. Section 5 evaluates our approach,

suggests possible countermeasures, and outlines some

future research directions. Finally, Section 6 con-

cludes this paper.

2 LITERATURE REVIEW

Throughout the transportation community, cybersecu-

rity vulnerabilities are a key concern for connected

vehicle (CV) applications. Bertini et al. (Bertini et al.,

2016) presented a survey result regarding the percep-

tion of connected and automated vehicle systems by

the Oregon Department of Transportation. Over a

hundred respondents were asked for potentials and

concerns about CV deployment in the survey. As a

result, 79% of responses were concerned about cy-

bersecurity risks where 50% of them inferred the sys-

tem is too premature to be implemented yet. The pa-

per concluded that CV applications may be beneﬁcial

given that safe communication is guaranteed. Kaur

et al. (Kaur et al., 2016) described potential security

attacks on CV technology and classiﬁed the attacks

based on the impacts of their outcomes. They also

provided a comprehensive view of security practices

and theoretical risk assessment for the attacks.

With increasing interests in cybersecurity, re-

searchers attempted to investigate an impact of cyber-

attacks and communication errors through simulation

platform. Bhavsar et al. (Bhavsar et al., 2017) in-

vestigated the risk of communication threat to the

mixed trafﬁc stream. The communication threat in

this paper were identiﬁed through fault tree model for

both vehicular component and infrastructure compo-

nent. Risk analysis was conducted through simula-

tion; however, the paper deﬁned the threat as commu-

nication failure probability from reviewing the litera-

ture. Cui et al. (Cui et al., 2018) proposed a simula-

tion platform for Cooperative Adaptive Cruise Con-

trol (CACC) vehicles. The platform improved the

quality of simulation considering vehicle dynamics,

communication uncertainty, quantifying crash sever-

ity and CACC stability, and so on. Cyber-attack in

this paper was deﬁned as randomly varying radar and

GPS sensor errors. Through sensitivity analysis, the

authors resulted GPS and radar sensor errors may cre-

ate a collision when the errors happened to be the

same direction (e.g., sensor error of positive or neg-

ative value) and GPS jamming is the most dangerous

cyber-attack. Heijden et al. (van der Heijden et al.,

2017) analyzed security attacks on CACC and their

impacts using simulation, suggesting the necessity

of mis-behavior detection along with resilient con-

trollers with graceful degradation.

When it comes to urban trafﬁc, cybersecurity

plays an even more critical role in road safety since

street intersections are where most vehicle collisions

happen. Ernst and Michaels (Ernst and Michaels,

2017) studied the severity of cyber vulnerability on

signalized intersections. They investigated possible

threats to the trafﬁc controller (e.g., changes on signal

phase and timing). Trafﬁc simulation was conducted

to measure the increase in travel time along with the

threat level. Feng et al. (Feng et al., 2018) inves-

tigated vulnerabilities of trafﬁc control system under

cyber-attacks based on falsiﬁed data. They assumed

the attacker’s goal is to maximize the system delay on

actuated and adaptive signal intersections. One of the

achievements was identifying the critical intersection

with the highest potential for heavy congestions.

In order to improve the security but not to

compromise the low-latency feature of DSRC, the

US Department of Transportation (USDOT) intro-

duced their Security Credential Management System

(SCMS). The Security Credential Management Sys-

tem (SCMS) is a proof-of-concept (PoC) message

security solution for vehicle-to-vehicle (V2V) and

vehicle-to-infrastructure (V2I) communication (US-

DOT, 2020b). This PoC uses a Public Key Infras-

tructure (PKI) approach that issues digital certiﬁcates

to authorized system participants to facilitate trusted

communication. This ensures the messages originate

from a legitimate source and guarantees the integrity

of the messages communicated. Amoozadeh et al.

(Amoozadeh et al., 2015) analyzed security vulner-

abilities of connected vehicles in the context of multi-

ple attacks whose impact was observed through sim-

ulation using VENTOS. They presented various types

of attacks including message falsiﬁcation in which the

adversary listens to the wireless communication be-

VEHITS 2020 - 6th International Conference on Vehicle Technology and Intelligent Transport Systems

tween vehicles and manipulates the contents of the

messages. However, due to the fact that the mes-

sages are modiﬁed by the adversary, the attack is

subjected to integrity checks from defense mecha-

nisms such as SCMS. Laurendeau et al. (Lauren-

deau and Barbeau, 2006) performed an analysis on

DSRC/WAVE architecture and implied that DSRC

is potentially vulnerable towards cyber-attacks such

as replay attacks where the adversary intercepts and

retransmits malicious messages. Recent work (Le

et al., 2019) presented some attacks that adversaries

can cost-effectively launch on DSRC. However, they

did not conduct experiments to evaluate the attacks

on the DSRC protocol. Motro et al. (Motro et al.,

2016) evaluated the effectiveness of the DSRC-based

V2V communication system in VANET simulation

environment. Various DSRC characteristics including

power settings, communication range, packet errors,

sensor errors, and estimation inaccuracy were tested

to detect collision on highway. Throughout the sim-

ulation, the authors concluded that the primary factor

to be considered is communication range. The ma-

jority of communication failures were caused by the

vehicle being out of the range for the power settings.

The research provided a promising result that packet

errors at a rate of up to 50% did not have a signiﬁcant

impact on collision detection. However, this research

is based on a strong assumption that their network is

secured.

State-of-the-art research studied the impacts of

cyber-attacks on transportation performance; how-

ever, their arguments are based on measurements ob-

tained through simulation platforms or probabilities

from literatures. In this paper, all experiments are

conducted with real On-Board Units (OBU) and a

Road Side Unit (RSU). Three possible cyber-attack

scenarios are presented and evaluated.

3 METHODOLOGY

DSRC is a wireless communication technology

speciﬁcally designed for high data transmission

among vehicles and infrastructures. DSRC deﬁnes

several sets of messages and ﬁelds for each message

which can be customized for V2X applications (US-

DOT, 2009). DSRC bandwidth vary by region. In

the U.S., the Federal Communications Commission

(FCC) allocated 75 MHz of spectrum in the 5.9 GHz

band for DSRC (FCC, 2019). Japan reserved 760

MHz of spectrum in the 5.8 GHz band for intelli-

gent transportation systems, in particular for DSRC

(Tachikawa, 2003). The European Union assigned 30

MHz of spectrum at 5.875 – 5.905 GHz for safety-

related applications and 20 MHz at 5.855 – 5.875

GHz for other applications (ACEA, 2018). DSRC

aims at providing a low latency protocol for Vehic-

ular Ad-Hoc Networks (VANETs) and V2X commu-

nication (Torabi and Ghahfarokhi, 2014). The main

goals for the protocol are to be reliable, fast, and safe

for both passengers and pedestrians. However, in this

work, we show that the DSRC protocol design has not

met the reliability and safety goals to be deployed in

real-world use. This section describes our approach

to evaluate the robustness of the DSRC protocol.

Threat Model. In our attack model, the adversary

has access to a vehicle with an On-Board Unit (OBU)

or to a Road Side Unit (RSU). It is reasonable to as-

sume that the adversary can have his/her vehicles or

devices with DSRC capabilities like a legitimate user

and exploit the vulnerabilities to attack others. Basi-

cally, any user can act maliciously. The goal of the

adversary is to jam the communication channels or

target other OBUs/RSUs to spread false information

among the vehicles, which will then lead to physical

damage and/or prevent other vehicles from commu-

nicating as a form of Denial-of-Service (DOS) attack.

The adversary may also position himself/herself to in-

tercept communication and relaying it giving the im-

pression that two units are in range. In this paper, we

inspect three different types of attacks targeting com-

munication layer as follows.

Communication Jamming. We explore how a ma-

licious RSU can jam the communication between two

OBUs (see Figure 1). In this scenario, the adversary

aims to disrupt communications between the victims

by decreasing the signal-to-noise ratio. This could

have very serious implications in the case where one

OBU is trying to transmit a safety critical message

and the other OBU cannot receive it properly.

Man-In-The-Middle (MITM) Forwarding. We

look into the case that a malicious RSU can trick

two OBUs into thinking they are within range of each

other by forwarding the messages (see Figure 2). This

attack would be dangerous to transportation opera-

tions such as platooning, which uses distance as a

safety measure.

False Alert. A malicious OBU that decides to

spread false information is just as dangerous as a ma-

licious RSU. In this case, the malicious OBU could

cause trafﬁc to reroute by sending messages announc-

ing a collision alert (see Figure 3). The ability to con-

trol how trafﬁc is rerouted is dangerous because the

Evaluating the Dedicated Short-range Communication for Connected Vehicles against Network Security Attacks

Figure 1: Communication Jamming Attack Scenario.

Figure 2: Man in the Middle Forwarding Attack Scenario.

victims could be rerouted through areas that are un-

safe. It is important to note that this attack considers

a malicious user sending messages instead of an at-

tacker spooﬁng other users’ messages. The original

messages are not modiﬁed between source and des-

tination, thus bypassing integrity checks of defense

mechanisms such as the Security Credential Manage-

ment System (SCMS) (USDOT, 2020b).

4 EVALUATION

In this section, we describe our evaluation of the pro-

posed attacks on DSRC. We used LocoMate products

made by Arada Systems, which was a leading auto-

motive technology recently acquired by Lear Corpo-

ration (Lear, 2015). In order to experiment the pro-

posed attacks, we use two LocoMate Mini 2 devices

as the OBUs and a Locomate Classic device as an

RSU. We control the RSU via Ethernet connection

and control the OBUs via Bluetooth connection. Fig-

ure 4 shows an overview of our setup.

Figure 3: False Alert Attack Scenario.

Figure 4: Experiment Setup to Evaluate Proposed Attacks

on DSRC Protocol.

4.1 Communication Jamming

In this attack, the goal is to prevent messages sent

by one OBU from reaching the other OBU (see Fig-

ure 1). The idea is that strategically placed malicious

RSUs could create issues by ﬂooding the communi-

cation channels (in our case the control channel).

We used the RSU as a jammer to try to block the

messages going from the OBU A (Unit A) to the OBU

B (Unit B). The two OBUs were placed within 2 me-

ters from each other. At ﬁrst, we attempted to manip-

ulate the power of the transmissions in order to make

the signal transmissions of the RSU much stronger

than the others but that had no effect on the drop rate

of the messages from Unit A to Unit B. Thus, we tried

increasing signiﬁcantly the number of messages being

sent by the RSU in an amount of time. The default

delay between each message was 100ms. We modi-

ﬁed the RSU’s transmission interval to 1ms, allowing

it to transmit 100 times more messages in the same

amount of time.

In our experiment, we also conﬁgured so that Unit

A and Unit C are both sending Basic Safety Message

VEHITS 2020 - 6th International Conference on Vehicle Technology and Intelligent Transport Systems

(BSM) but with different vehicle length information

in order to differentiate them. More speciﬁcally, the

messages being sent from Unit A to Unit B will in-

clude an additional byte C8 as shown in Figure 5.

Figure 5: Basic Safety Message Sent by Unit a with the

Additional Byte C8 in the End.

Before deploying the jamming attack (i.e., mak-

ing the modiﬁcation to the transmission interval), we

calculated the message loss percentage (i.e., number

of messages that are not received out of the total of

sent messages) from Unit A to Unit B and found it to

be 19%. We ﬁrst found that the change in the trans-

mission interval of the malicious RSU dramatically

increased the message loss between OBU A and B to

more than 50% when launching the attack (as can be

seen in Table 1) indicating that the RSU (i.e., the jam-

mer) can prevent a large proportion of the messages

from reaching Unit B. We further observed the mes-

sage reception rate of Unit B. The attack reduced the

reception rate (from Unit A to B) by about a factor

of 10 (10.4 msg/sec and 1.1 msg/sec respectively). In

Table 2, we show the average number of messages

per second that Unit B received from both message

sources (i.e., from Unit A and the jammer) before and

after launching the jamming attack.

4.2 Man In The Middle (MITM)

Forwarding

In this attack, the goal is to make two OBUs (Unit

A and Unit B) believe that they are within range

of each other and communicate through a malicious

RSU (Unit C) which can eavesdrop on the messages,

vehicle information and/or cause misinformation (see

Figure 2).

We ﬁrst measured the maximum range in which

our two OBUs could communicate with each other.

This range is estimated to be 20 feet without line of

sight. For the experiment, we placed the two OBUs

out of their range. We then used the RSU as the “mid-

dle man” to forward the messages from one OBU to

the other. We found that the RSU was able to relay

the messages and tamper with them as they were not

encrypted or signed with security credentials. This

attack shows that it is possible to interfere with vehic-

ular applications such as platoon. In particular, this

attack can lead to unexpected merging or changes in

speed of the vehicles on the road, thus causing trafﬁc

chaos.

4.3 False Alert

In this attack, the attacker’s goal is to exploit an OBU

(unit A) to spread false information to the other OBU

(Unit B) and the RSU (Unit C) (see Figure 3). The

success of this attack means that a malicious OBU

could reroute trafﬁc or cause another vehicle brake

unexpectedly.

We found that a malicious actor could send mes-

sages containing false information to other units and

change the contents of the alerts and ﬁelds to arbi-

trary values. In Figure 6, we show an Intersection

Collision Alert (ICA) message being received by the

RSU (identical message received by the other OBU).

Similar to the BSM shown in Figure 5, this message

can be modiﬁed. There are also other types of mes-

sages used for critical alerts that can be sent. Those

available message types that can be exploited for a

false alert/information attack are: Intersection Colli-

sion Alert (ICA), Probe Vehicle Date (PVD), Basic

Safety Message (BSM), and Road Side Alert (RSA).

Figure 6: Intersection Collision Alert Message.

Depending on the way the connected vehicles han-

dle the reception of these alerts, the attack can be very

effective or less effective. For example, it is reason-

able to assume that when an ICA message is received,

the vehicle would certainly brake to prevent the col-

lision. Obviously, this critical message must be sent

and received with the smallest latency possible (thus

the possibility of sending it without registration to an

application). Therefore, being able to manually send

this type of message or modify it arbitrarily is dan-

gerous. For example, someone who would like their

route to be less congested could issue alerts to reroute

the other vehicles to alternative routes resulting in un-

balanced trafﬁc and congestion. Although sensors at-

tached to the vehicle (e.g., radar) may help with iden-

Evaluating the Dedicated Short-range Communication for Connected Vehicles against Network Security Attacks

Table 1: Message Loss in Normal Operation and in Presence of Jamming Attack.

Messages Sent by Unit A Message Received by Unit B Message Loss

Normal Operation 393 318 19.0%

Jamming Attack 193 106 54.4%

Table 2: Message Reception Rate of Unit B before and after Jamming.

Message Source

Unit B’s Reception Rate (messages/second)

Before Jamming After Jamming

From Unit A 10.40 1.10

From The Jammer 0 12.81

tifying false alerts, researchers have shown that sen-

sors are vulnerable to spooﬁng attacks in which the

adversary can easily manipulate outputs of the sen-

sors (Fu and Xu, 2018).

5 DISCUSSION

We have shown that DSRC is vulnerable to the three

network security attacks. In this section, we discuss

some limitations of our work. We also describe po-

tential defenses that can be adopted to enhance the

security of DSRC. Finally, we outline some future re-

search directions to extend our work.

5.1 Limitation

5.1.1 Scalability

The scalability of our attacks needs to be further re-

searched. Our experiments were conducted using Lo-

coMate devices (as described in Section 4). There can

be other different manufacturers producing DSRC in-

frastructures and devices, which may introduce dif-

ferent variants of DSRC-supported hardware. Al-

though these variants may vary in capabilities, we fo-

cus on evaluating DSRC rather than other capabilities.

Besides, in our attack experiments, we use station-

ary hardware setup due to testing environment con-

straints. Hence, some attacks may produce different

outcomes when it comes to real vehicles in motion.

However, we believe that this does not undermine our

overall ﬁndings and insights in this paper.

5.1.2 Security Credential Management System

Our proposed attacks reasonably assumed that SCMS

was not in use because of the following reasons. First,

SCMS is still a new technology under research and

development. We tried to send an enrollment request

to the USDOT. However, the access to enroll in the

SCMS was limited to research deployment sites that

receive funding from the USDOT (USDOT, 2020b).

In addition, it is important to note that although se-

curity credentials issued by defense mechanisms such

as SCMS may protect the authenticity and integrity

of the messages, it can be difﬁcult to defend against

malicious users who possess several legitimate cre-

dentials and exploit their own credentials to launch

attacks. In fact, assuming that units A, B, and C

have valid credentials, all the aforementioned attacks

are still possible. SCMS does not prevent users from

changing message transfer rate, relaying messages, or

sending messages that are not consistent with reality.

Although, in the case of the MITM forwarding attack,

SCMS might be able to prevent the malicious unit C

from modifying the messages in between the source

and the destination.

5.1.3 Testing on Real Vehicles

There are few vehicles actually equipped with DSRC.

Many have hardware support for it but it is not nec-

essarily supported by the car’s manufacturer software

yet. However, the USDOT has already equipped some

trucks and infrastructure elements in order to test

DSRC via pilot programs in Wyoming, Florida, and

New York (USDOT, 2020a). Also, testing security

attacks on a real vehicle in motion could be very dan-

gerous if the vehicle has some of the basic responses

to DSRC messages and alerts implemented (e.g., hard

stop while in high speed motion due to a collision

alert). For this reason, we decided to conduct our ex-

periments with stationary hardware setup instead.

5.2 Potential Countermeasures

There are two solutions we suggest to prevent net-

work congestion-based types of attack. The ﬁrst

would be to set a limit for the message transmission

interval so that legitimate RSU and OBU devices can-

not be exploited for spamming messages. The sec-

ond solution is to monitor the network trafﬁc to de-

VEHITS 2020 - 6th International Conference on Vehicle Technology and Intelligent Transport Systems

tect anomaly and prevent attackers from ﬂooding the

message queue. The limitation of this defense is that

it may increase latency and it is not effective against

distributed attacks which use a large number of ex-

ploited devices. In practice, both defense mechanisms

should be combined to increase the coverage against

different types of attack.

Integrity check in defense mechanisms such as the

SCMS will be useful in providing a way of ensuring

that the messages are not modiﬁed on the way; how-

ever, it will neither prevent the attacker from relaying

the messages to an unexpected destination nor prevent

the attacker from sending originally malicious mes-

sages. One solution to prevent the MITM forward-

ing attack is to measure the time it takes at maximum

range to transmit a message. If the difference between

the sent time and the received time is too great then

that message should be marked as malicious item (i.e.,

distance bounding).

In order to prevent the false alert attack, anomaly

detection is again a great method. Once malicious be-

haviors are detected, the adversary’s credential must

be suspended. As part of the system, all vehicles need

to maintain a list of revoked credentials to avoid ad-

versaries. However, there might be a tradeoff between

security and user experience since the accuracy of the

anomaly detection algorithm matters. It is important

to have an accurate detection system to avoid losing

too much user experience.

5.3 Future Work

5.3.1 DSRC Integrated with Visual

Classiﬁcation

Autonomous vehicles now are implemented with vi-

sual classiﬁcation technology in the system to recog-

nize road signs and trafﬁc lights. Methods like deep

neural networks (DNNs) are capable of doing that

with the help of sensors such as camera and lidar. Vi-

sual classiﬁcation based on DNNs has been proven to

be vulnerable to adversarial machine learning exam-

ples, which would lead to dangerous attacks such as

tricking a vehicle to mistake a stop sign as a speed

limit sign. However, integrating data and the deci-

sions based on DSRC and visual classiﬁcation could

help detect the adversaries and mitigate the threats. It

would be much harder for the attacker to successfully

spoof on both channels. Designing this type of inte-

grated mechanisms and testing against attacks will be

meaningful.

5.3.2 DSRC with Security Certiﬁcates Deployed

With security certiﬁcates implemented along with

DSRC, our intuition is that the jamming and the false

alert attacks can still be possible. However, after the

incident happens, it might be easier to identify the

culprit thanks to the certiﬁcates attached to the ma-

licious messages. In the MITM forwarding attack the

malicious actor will be able to forward the message,

but not modify its content. In this case, the certiﬁ-

cates will not prevent the incident but will be useful

to trace the adversary after the attack happens. In the

false alert attack, the attacker will still be able to cre-

ate custom messages but will need to sign the message

with a valid certiﬁcate.

5.3.3 Defense Evaluation

Our proposed defenses need to be further evaluated

for each attack to validate their performance against

the attack scenarios discussed in this paper. It is also

important to evaluate the feasibility of our defenses,

considering possible interference with other critical

aspects of the protocol implementation such as la-

tency.

5.3.4 Attack Implementation in Simulation

Simulation allows us to evaluate the attacks in a bet-

ter controlled environment. In addition, we can com-

pare the results between real hardware and simulation

testing. A simulated environment also enables attack

implementations without worrying about some phys-

ical limitations, for example, we had the constraints

on power supply for the RSU when setting up the ex-

periments. Moreover, a simulated environment may

give us an opportunity to study the performance of the

attacks without having to worry about hardware com-

patibility issues. Furthermore, a comprehensive sim-

ulator that enables modiﬁcations to the DSRC imple-

mentation may help us better evaluate our proposed

countermeasures.

6 CONCLUSION

With the active growth of autonomous vehicles and

the goal of improving automotive safety, a robust

wireless communication technology used for con-

nected vehicles is very important. Due to its safety-

critical use case, this technology needs to be carefully

evaluated and tested prior to deployment for real-

world use. In this paper, we have shown that DSRC,

which is a popular wireless communication technol-

ogy designed for connected vehicles, is vulnerable to

Evaluating the Dedicated Short-range Communication for Connected Vehicles against Network Security Attacks

three network security attacks: jamming, MITM for-

warding, and false alert. In the near future, connected

and fully autonomous vehicles will soon replace tra-

ditional vehicles. As such, more attack surfaces will

lead to more safety issues. Therefore, it is important

to ensure that we have effective defense mechanisms

in the ﬁrst place.

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