To Act or Not to Act: An Adversarial Game for Securing Vehicle Platoons

Vehicle platooning systems are vulnerable to malicious attacks that exploit vehicle-to-vehicle (V2V) communication, causing potential instability and increased collision risks. Conventional machine learning (ML) detection methods show promise but can be circumvented by intelligent adversaries. In this paper, we present a novel, end-to-end attack detection and mitigation approach that uniquely incorporates advancements in (adversarial) machine learning, control theory, and game theory. We employ a non-cooperative security game with imperfect information to model complex attack/defense interactions. This aids in making informed decisions regarding detector deployment and attack mitigation, even amidst possibly misleading attack detection reports. We model our control system reconfiguration attack mitigation approach as a switched system and provide a n in-depth stability analysis. The simulations conducted in a sophisticated simulator demonstrate our approach's potential for real-world online deployment. Our game-based defense formulation significantly improves inter-vehicle distance and defense utilities against both cyber-physical and adversarially-masked attacks while reducing the distance disturbance caused by the ambient traffic by up to 87% compared to baseline defense approaches.