Detection, Identification, and Mitigation of False Data Injection Attacks in Vehicle Platooning

Abstract

Vehicle platooning has gained significant attention due to its potential to enhance road safety, fuel efficiency, and traffic flow. However, the reliance on interconnected communication technology in platooning necessitates robust cybersecurity measures. This paper introduces frameworks for detecting and identifying cyber-attacks, specifically False Data Injection Attacks (FDIAs), aimed at securing vehicle platoons. To achieve this objective, a state-space model is developed, capable of accommodating any information flow topology and any number of vehicles within the platoon. To estimate the internal states of each vehicle, an Unknown Input Observer (UIO) is proposed. The detection of attacks on each vehicle is accomplished by employing a dedicated Detection UIO designed to detect FDIA on the respective vehicle. Furthermore, an Identification UIO is designed to identify compromised parameters of the attacked vehicle and mitigate the attacks by replacing the compromised parameters with their estimated authentic values. The effectiveness of the proposed approach is demonstrated through MATLAB simulations, encompassing various platooning configurations and attack scenarios. The simulation results highlight the accuracy of attack detection, particularly under stealthy attacks, and the successful identification of compromised vehicles.