Robust Cooperative Sparse Representation Solutions for Detecting and Mitigating Spoofing Attacks in Autonomous Vehicles

Abstract

The new era of Industry 4.0 and its key-enabling Internet of Things technologies promises fundamental advances during data collection, processing and analysis from a variety of agents and sensors, for the collective benefit of society. In this regard, connected and autonomous vehicles equipped with integrated perception sensors and communication abilities formulate a cluster or swarm of intelligent nodes capable to transform the transportation sector into a new smart mobility system. However, its feasible operation may be potentially threatened by hijackers whose goal is to cause malfunctioning to critical vehicular sensors, harnessing the perception system of vehicle. Therefore, in this paper we discuss the impact of cyberattacks such as GPS spoofing on autonomous vehicles, and design efficient detection and mitigation centralized schemes which provide location awareness and security monitoring over the whole cluster of vehicles. More specifically, we exploit the cooperation among the interacting vehicles, and develop robust sparse coding solutions based on graph signal processing and Alternating Direction Method of Multipliers. Cooperative based approach is further benefited by a in-vehicle module which provides spoofing detection alerts at the level of individual vehicle. Experimental analysis using the renowned CARLA simulator indicates highly efficient mitigation performance for different rates of compromised vehicles, as well as spoofing detection metrics greater than 94%.