FedAV: Federated learning for cyberattack vulnerability and resilience of cooperative driving automation

Abstract

Cooperative driving automation (CDA) has gained attention over the years because of its cooperative driving capability that provides solution to individual automated driving challenges. Although reliance on communication and automation enables cooperative driving, it also introduces new cybersecurity threats. This study introduces a federated learning concept for autonomous and connected vehicles, known as the federated agents on vehicle platooning (FedAV) framework, which is designed to address the challenges of cyberattack simulations and anomaly detection in cooperative vehicle platooning systems. The federated learning approach was adopted because of its decentralized nature, which allows each vehicle to learn independently with the ability to overcome adversarial attacks. First, FedAV employs a mixed cyberattack simulation approach to capture complex attack patterns effectively. We tested the scalability of our approach against several attacks, including spoofing, message falsification, and replay attacks, as well as on anomalies, including short anomalies, noise anomalies, bias anomalies, and gradual shifts. In addition, our approach integrates federated learning for decentralized anomaly detection, ensuring data privacy and reducing communication overhead. The anomaly detection performance was enhanced by average and weighted aggregation strategies. Real-world scenarios from cooperative driving experiments and simulations validated the framework's effectiveness and demonstrated its potential to improve the safety, privacy, and efficiency of CDA.