GPS Attack Detection and Defense for Secure Localization of Automated Vehicles Based on Vehicle-to-Vehicle Technology

Accurate and stable localization system plays a significant role in safe driving for connected automated vehicles (CAVs). However, the vulnerability from GPS spoofing attacks undermines the security of the localization system, posing great challenges for autonomous driving. In this article, a security-critical study for anomaly detection and defense against GPS attacks for CAVs using vehicle-to-vehicle (V2V) technology is explored to improve the localization for driving safety under cyber-attack. First, a robust learning-based GPS stealthy attack model is designed to generate spoofing GPS signals, which can evade currently widely applied Kalman filter-based localization with $\chi ^{2}$ anomaly detector and result in vehicle positioning errors, leading to more potential driving hazards than traditional models. Then a novel detection method for GPS anomaly with V2V communication based on density clustering algorithm is proposed to detect the wrong GPS data effectively. When the GPS attack is detected, the vehicle position is estimated accurately by an innovative cooperative localization (CL) approach with multi-information fusion from neighboring vehicles to defend against the GPS attack. The proposed framework is evaluated with three real-world driving data sets in closed-loop simulation. The results show that the developed attack detection method has the best performance compared to the state-of-the-art methods in terms of detection accuracy and detection timeliness. Furthermore, the CL for attack defense can provide accurate position estimation for the victim vehicle against GPS attack to realize safe autonomous driving, illustrating the effectiveness and robustness in various driving scenarios.