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Abstract
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 road-side 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.
Highlights
R ECENTLY, autonomous driving has obtained many achievements and progresses towards Level 5 of the SAE J3016 standard [1]
We propose a cooperative signalbased verification scheme to enhance the reliability of data sharing in vehicular networks
Due to the deep relevance to physical signal processing, we present the details of communication environment assumption, antenna configuration of the vehicle on-board units, communication channel, and the geometry model of the vehicles’ relative locations
Summary
R ECENTLY, autonomous driving has obtained many achievements and progresses towards Level 5 (i.e., fully autonomous) of the SAE J3016 standard [1]. The authors of [10] proposed to collect the radar checks of neighboring vehicles to identify an honest vehicle Another common type of this approach is the trust-based detection [11]–[15], which includes reputation mechanisms to vote on the correctness of the information. In [20], the authors proposed a plausibility check on the frequency of messages received at the host vehicle and the estimated value by Doppler radar and cameras Their approach did not tackle Sybil attacks. The detection engines are capable of signal-based verification and respond quickly to the attacks In this core function, the values of the target vehicle’s six-dimensional state (longitude, latitude, velocity, heading, acceleration, and yaw rate) extracted from the Cooperative Awareness Messages (CAM) (defined in the ETSI standard [23]) are crosschecked by the Student’s t-test with the corresponding values estimated by an unscented Kalman filter (UKF) in the time series.
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