Detection and Mitigation of GPS Attack via Cooperative Localization

Connected automated vehicles (CAVs) share information through vehicular networks; however, cyber-attacks on GPS may cause significant challenges to compromise vehicle security and driving safety. In this paper, a novel approach for GPS attack detection and mitigation is proposed using vehicle-to-vehicle (V2V) communication, which enables vehicles to access and utilize accurate location information for autonomous driving. Instead of directly fusing the location data received from other vehicles, a trust evaluation process with a $\chi$2-detector is developed to identify and isolate potential malicious surrounding vehicles that may send erroneous information into the V2V network. Subsequently, a Bayesian approach is employed to fuse data from GPS, inter-vehicle distance, and bearing angle measurements. A real-time Robust-Random-Cut-Forest based detector is constructed to identify possible GPS attacks for an ego vehicle. When a malicious attack is detected, a novel cooperative positioning method is used to mitigate the impact of the GPS attack based on V2V information. Simulation results demonstrate the performance of the proposed approach in detecting GPS attacks timely and improving the positioning accuracy and robustness of CAVs under different attacks.