Real-time False Data Injection Attack Detection in Connected Vehicle Systems with PDE modeling

Connected vehicles as a promising concept of Intelligent Transportation System (ITS), are a potential solution to address some of the existing challenges of emission, traffic congestion as well as fuel consumption. To achieve these goals, connectivity among vehicles through the wireless communication network is essential. However, vehicular communication networks endure from reliability and security issues. Cyber-attacks with purposes of disrupting the performance of the connected vehicles, lead to catastrophic collision and traffic congestion. In this study, we consider a platoon of connected vehicles equipped with Cooperative Adaptive Cruise Control (CACC) which are subjected to a specific type of cyber-attack namely "False Data Injection" attack. We developed a novel method to model the attack with ghost vehicles injected into the connected vehicles network to disrupt the performance of the whole system. To aid the analysis, we use a Partial Differential Equation (PDE) model. Furthermore, we present a PDE model-based diagnostics scheme capable of detecting the false data injection attack and isolating the injection point of the attack in the platoon system. The proposed scheme is designed based on a PDE observer with measured velocity and acceleration feedback. Lyapunov stability theory has been utilized to verify the analytically convergence of the observer under no attack scenario. Eventually, the effectiveness of the proposed algorithm is evaluated with simulation study.