The Security of Autonomous Driving: Threats, Defenses, and Future Directions

Ren, Kui; Wang, Qian; Wang, Cong; Qin, Zhan; Lin, Xiaodong

doi:10.1109/jproc.2019.2948775

Cited by 164 publications

(87 citation statements)

References 66 publications

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“…1) Sensing security: As the eye of autonomous vehicles, the security of sensors is nearly essential. Typically, jamming attacks and spoofing attacks are two primary attacks for various sensors [166], [167]. For example, the spoofing attack generates an interference signal, resulting in a fake obstacle captured by the vehicle [168].…”

Section: Security and Privacymentioning

confidence: 99%

“…However, this also leads to new attack surfaces with various attack methods, such as jamming attacks, replay attacks, relay attacks, etc. [166]. For example, the attacker could capture the communication between key and door and replay it to open the door [188].…”

Section: Security and Privacymentioning

confidence: 99%

“…Attacks and defenses are always opposites, and absolute security does not exist. The emerging CAVs face many security challenges, such as reply attacks to simulate a vehicle's electronic key and spoof attacks to make vehicle detour [188], [166]. With the integration of new sensors, devices, technologies, infrastructures, applications, the attack surface of CAVs is further expanded.…”

Section: Cyberattack Protectionmentioning

confidence: 99%

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Edge Computing for Autonomous Driving: Opportunities and Challenges

Liu¹,

et al. 2019

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The recent proliferation of computing technologies, e.g., sensors, computer vision, machine learning, hardware acceleration, and the broad deployment of communication mechanisms, e.g., DSRC, C-V2X, 5G, have pushed the horizon of autonomous driving, which automates the decision and control of vehicles by leveraging the perception results based on multiple sensors. The key to the success of these autonomous systems is making a reliable decision in a real-time fashion. However, accidents and fatalities caused by early deployed autonomous vehicles arise from time to time. The real traffic environment is too complicated for the current autonomous driving computing systems to understand and handle. In this paper, we present the state-of-the-art computing systems for autonomous driving, including seven performance metrics and nine key technologies, followed by eleven challenges and opportunities to realize autonomous driving. We hope this paper will gain attention from both the computing and automotive communities and inspire more research in this direction.

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Section: Security and Privacymentioning

confidence: 99%

Section: Security and Privacymentioning

confidence: 99%

Section: Cyberattack Protectionmentioning

confidence: 99%

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Edge Computing for Autonomous Driving: Opportunities and Challenges

Liu¹,

et al. 2019

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“…A malicious or compromised vehicle may transmit false data and then fool the receivers, thereby potentially causing safety hazards and severe accidents. Such attacks are common and well acknowledged in the literature [2], including in V2X networks [3]. Since the host vehicle's decision-making system may rely much on the shared data, particularly in non-lineof-sight (NLOS) areas and harsh environments, malicious vehicles must be detected and ejected from the network.…”

Section: Introductionmentioning

confidence: 99%

“…Since the host vehicle's decision-making system may rely much on the shared data, particularly in non-lineof-sight (NLOS) areas and harsh environments, malicious vehicles must be detected and ejected from the network. Also, the high-definition (HD) map building process in selfdriving vehicles often relies on the shared information [2]; thus, shielding the receivers against an unreliable data source is worthwhile.…”

Section: Introductionmentioning

confidence: 99%

A Beamforming Signal-Based Verification Scheme for Data Sharing in 5G Vehicular Networks

2020

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Vehicle-to-Everything (V2X) communications are vital for autonomous vehicles to share sensing data about the surrounding environment, particularly in non-line-of-sight (NLOS) areas where the camera and radar systems often perform poorly. However, an insider adversary such as a compromised vehicle can disseminate false sensing data that even a signature scheme cannot counter. Trusting such shared data, the surrounding vehicles may be trapped to react unexpectedly and potentially poses the risk of a fatal crash. In this work, we introduce a prospective cooperative verification scheme to support both the host vehicles and V2X edge applications in validating the truthfulness of sharing data in the fifth-generation (5G) vehicular networks. First, the detection systems at the host vehicle (local detector) and the roadside unit (RSU) (global detector) separately recreate a trajectory of a target vehicle by extracting its status and attributes from the received Cooperative Awareness Messages (CAM). Simultaneously, they also build another trajectory of the vehicle by independently fusing real-time measurement metrics from signal-based positioning. We then perform a Student's t-test to detect any significant differences between the extracted trajectory and the corresponding measured one. Finally, the quantified evidence from the local and global detector tests will be fused through the Dempster-Shafer fusion for the final decision, i.e., whether the target vehicle is trustful. Besides the theoretical analysis of basic limits, we perform extensive evaluations of the work in cases of both sparse and heavy traffic densities. Through the simulation, this work demonstrates its significant effect in terms of detection performance and response time, particularly for detecting Sybil and false data attacks quickly.

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