An Overview of Attacks and Defences on Intelligent Connected Vehicles

Dibaei, Mahdi; Zheng, Xi; Jiang, Kun; Maric, Sasa; Abbas, Robert; Liu, Shigang; Zhang, Yuexin; Deng, Yao; Wen, Sheng; Zhang, Jun; Xiang, Yang; Yu, Shui

doi:10.48550/arxiv.1907.07455

Cited by 7 publications

(10 citation statements)

References 142 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A jamming attack system is established for a type of a WNCS targeting each sensor in the system [167]. A few major security attacks on the intelligent connected vehicles is identified [168]. A comprehensive survey of available defences against these attacks is discussed on intelligent vehicle systems.…”

Section: Spoofing Attacksmentioning

confidence: 99%

Analysis and Challenges in Wireless Networked Control System: A Survey

Hamdan

Mahmoud

2022

IJRCS

View full text Add to dashboard Cite

A wireless networked control system (WNCS) consists of a dynamic system to be controlled, sensors, actuators, and a remote controller. A WNCS has two types of wireless transmissions, i.e., the sensor's measurement transmission to the controller and the controller's command transmission to the actuator. In this paper, we are surveying the literature on the communication networks in WNCSs and the challenges related to them, such as the communication standards, delay, Packet dropout, and delay jitter. Then, the control approaches in the design of a WNCS are presented, including the interactive design approaches and the joint design approaches. Also, several applications of WNCSs have been discussed in terms of their structure, functionality, and control design. These applications include Intra-Vehicle Wireless networks, Wireless Avionics Intra-Communication, Building Automation, and Water pumping. After that, security issues in WNCSs from a control engineering point of view are detailed while focusing on the major kinds of cyber attacks affecting WNCSs. Finally, future directions and conclusions are summarized at the end of the paper.

show abstract

Section: Spoofing Attacksmentioning

confidence: 99%

Analysis and Challenges in Wireless Networked Control System: A Survey

Hamdan

Mahmoud

2022

IJRCS

View full text Add to dashboard Cite

show abstract

“…Wi-Fi, WiMAX, Long-Term Evolution (LTE), Near-Field Communication (NFC), and Dedicated Short-Range Communications (DSRC) are among the communication technologies available for vehicular data communications. In reviewing the literature, several attacks on the communication network involving ITS were found [2]. In terms of the privacy, there are various types of inference attacks, for instance, parameter inference, input inference, and attribute inference attacks, which can jeopardize the privacy of the vehicles.…”

Section: B Cybersecurity Of Fl In Wireless Mobile Networkmentioning

confidence: 99%

“…Recent reports identified highly practical wireless attacks on CAVs [2]. Some attacks target in-vehicle security and others target security of inter-vehicle communications [3], [4].…”

Section: Introductionmentioning

confidence: 99%

Cybersecurity Threats in Connected and Automated Vehicles based Federated Learning Systems

Mallah¹,

Badu-Marfo²,

Farooq³

2021

Preprint

View full text Add to dashboard Cite

Federated learning (FL) is a machine learning technique that aims at training an algorithm across decentralized entities holding their local data private. Wireless mobile networks allow users to communicate with other fixed or mobile users. The road traffic network represents an infrastructurebased configuration of a wireless mobile network where the Connected and Automated Vehicles (CAV) represent the communicating entities. Applying FL in a wireless mobile network setting gives rise to a new threat in the mobile environment that is very different from the traditional fixed networks. The threat is due to the intrinsic characteristics of the wireless medium and is caused by the characteristics of the vehicular networks such as high node-mobility and rapidly changing topology. Most cyber defense techniques depend on highly reliable and connected networks. This paper explores falsified information attacks, which target the FL process that is ongoing at the RSU. We identified a number of attack strategies conducted by the malicious CAVs to disrupt the training of the global model in vehicular networks. We show that the attacks were able to increase the convergence time and decrease the accuracy the model. We demonstrate that our attacks bypass FL defense strategies in their primary form and highlight the need for novel poisoning resilience defense mechanisms in the wireless mobile setting of the future road networks.

show abstract

“…Most of these vulnerabilities are related to the CAN bus protocol, which lacks security protections such as encryption and authentication [2]. Additionally, expansions to vehicular communication present another avenue for compromise [11]. Arguably, the next generation of AVs is likely to be at even higher risk to these types of attacks, possibly at widescale compromise, all while more control is shifted away from drivers to automated systems on the vehicle.…”

Section: Related Workmentioning

confidence: 99%

Compromised ACC vehicles can degrade current mixed-autonomy traffic performance while remaining stealthy against detection

Gunter¹,

Li²,

Hojjati³

et al. 2021

Preprint

View full text Add to dashboard Cite

We demonstrate that a supply-chain level compromise of the adaptive cruise control (ACC) capability on equipped vehicles can be used to significantly degrade system level performance of current day mixed-autonomy freeway networks. Via a simple threat model which causes random deceleration attacks (RDAs), compromised vehicles create congestion waves in the traffic that decrease average speed and network throughput. We use a detailed and realistic traffic simulation environment to quantify the impacts of the attack on a model of a real high-volume freeway in the United States. We find that the effect of the attack depends both on the level of underlying traffic congestion, and what percentage of ACC vehicles can be compromised. In moderate congestion regimes the attack can degrade mean commuter speed by over 7%. In high density regimes overall network throughput can be reduced by up to 3%. And, in moderate to high congestion regimes, it can cost commuters on the network over 300 U SD kmhr . All of these results motivate that the proposed attack is able to significantly degrade performance of the traffic network.We also develop an anomaly detection technique that uses GPS traces on vehicles to identify malicious/compromised vehicles. We employ this technique on data from the simulation experiments and find that it is unable to identify compromised ACCs compared to benign/normal drivers. That is, these attacks are stealthy to detection. Stronger attacks can be accurately labeled as malicious, motivating that there is a limit to how impactful attacks can be before they are no longer stealthy.Finally, we experimentally execute the attack on a real and commercially available ACC vehicle, demonstrating the possible real world feasibility of an RDA. In particular, we test two wellknown CAN bus detection techniques on the ensuing data and find that they fail to identify the malicious messages, suggesting that the attacks may be stealthy to detection at the vehicular level as well. These results suggest that current-day mixed-autonomy traffic may be vulnerable to cyber-attacks that can degrade system-level performance, all while remaining stealthy against detection.

show abstract

An Overview of Attacks and Defences on Intelligent Connected Vehicles

Cited by 7 publications

References 142 publications

Analysis and Challenges in Wireless Networked Control System: A Survey

Analysis and Challenges in Wireless Networked Control System: A Survey

Cybersecurity Threats in Connected and Automated Vehicles based Federated Learning Systems

Compromised ACC vehicles can degrade current mixed-autonomy traffic performance while remaining stealthy against detection

Contact Info

Product

Resources

About