Real-time detection and estimation of denial of service attack in connected vehicle systems

Advanced connectivity features in today's smart vehicles are giving rise to several promising intelligent transportation technologies. Connected vehicle system is one among such technologies, where a set of vehicles can communicate with each other and the infrastructure via communication networks. Connected vehicles have the potential to improve the traffic throughput, minimize the risk of accidents and reduce vehicle energy consumption. Despite these promising features, connected vehicles suffer from the safety and security issues. Especially, vehicle-to-vehicle and vehicle-to-infrastructure communication make the connected vehicles vulnerable to cyber attacks. In order to improve safety and security, advanced vehicular control systems must be designed to be resilient to such cyber attacks. The first step of designing such attack-resilient control system is detection of the occurrence of the cyber attack. In this paper, we address this need and propose a real-time scheme that can potentially 1) detect the occurrence of a particular cyber attack, namely denial of service; and 2) estimate the effect of the attack on the connected vehicle system. The scheme consists of a set of observers, which are designed using sliding mode and adaptive estimation theory. The mathematical convergence properties of the observers are analyzed via Lyapunov's stability theory. Finally, simulation demonstrates the performance of the approach and the robustness of the scheme under several forms of uncertainties.