xFedCAV: Cyberattacks on Leader and Followers in Automated Vehicles With Cooperative Platoons Using Federated Agents

The increasing prevalence of connected and autonomous vehicles (CAVs) in smart cities requires robust cyberattack and anomaly detection systems to ensure safety and resilience. Cyberattacks on leader and follower in cooperative driving can result in differing impacts, however, their impacts on security and resilience of cooperative driving are largely unknown. Traditional anomaly detection methods, which aggregate data centrally, compromise driver privacy and are insufficient to address real-world challenges due to limitations of being compromised by adversarial attacks. To overcome these limitations, we propose Explainable Fine-Grained Cyberattacks and Anomaly Detection with Federated Agents for connected autonomous vehicles (xFedCAV). Our framework leverages federated learning to enhance privacy and security, using Shapley Additive exPlanations (SHAP) for interpretable detection. Unlike existing methods, xFedCAV focuses on fine-grained detection by simulating cyberattacks on individual vehicles rather than the entire fleet, allowing for more precise identification and response. Experimental results, conducted on a real-world CAV dataset, demonstrate that xFedCAV not only explains the relationship between vehicle characteristics and detection outputs, but also effectively detects cyberattacks in a decentralized manner. This research offers knowledge about the cybersecurity impacts of the leader and follower within cooperative driving and provides a significant advancement in federated learning applications for CAVs, contributing to the development of safer and more resilient smart city applications for transportation systems.