Abstract: The recent integration of vehicle-to-everything (V2X) communication, Internet of Things (IoT), and smart grid infrastructures in the transportation domain has resulted in significant security threats owing to the high degree of inter-connectedness, various data sources, and the need to ensure timely responses. Existing security techniques are mostly based on centralized approaches or domain-centric techniques. These approaches have resulted in scalability limitations, compromised data privacy, and lower robustness to new zero-day cyber threats. Although recent approaches have adopted the idea of federated learning to address the issue of data privacy, existing techniques have mostly been based on single domain-centric approaches. In order to deal with the aforesaid challenges, a new framework named ‘Cross-Domain Federated Adversarial Learning,’ abbreviated as ‘CFAL,’ has been conceived with the aim of facilitating unified intrusion detection for V2X, IoT, and Smart Grid Systems. The most important aim of the CFAL framework is to ensure the improved robustness against various types of attacks with minimal latency and maximum dependability. The CFAL framework has been suggested with the aim of integrating the Federated Learning technique with the Generative Adversarial Learning technique. Moreover, a cross-domain learning technique was utilized. The proposed CFAL framework was evaluated based on accuracy, zero-day detection rate, false alarm rate, latency, reliability, and availability. As presented in the experimental results, CFAL significantly improves the detection of zero-day attacks while ensuring a high accuracy rate, low false alarm rate, near-real-time system latency, and high system reliability and availability. This clearly shows that CFAL is a promising security solution for next-generation Cyber-Physical Systems.

Key words: adversarial training, data injection attack, distributed V2X, federated learning, internet of things, security threat