Robust Intrusion Detection System for Vehicular Networks: A Federated Learning Approach Based on Representative Client Selection

The rapid development of network technology has allowed numerous vehicular applications to be deployed in vehicles, thereby enriching the driving experience of users. However, the openness of vehicular networks enables attackers to launch network attacks on vehicles through network ports, leading to the destruction of vehicular networks. To develop an intrusion detection system suitable for distributed vehicular networks, researchers have utilized federated learning to train detection models. Nevertheless, most federated learning-based vehicular intrusion detection systems seldom consider rapidly updating the detection model and fail to detect unknown attacks effectively. In this study, we propose a federated learning-based vehicular intrusion detection system that fully considers the traffic characteristics of multiple network regions and selects representative clients to participate in model aggregation, thereby accelerating the convergence of the global model. Furthermore, to enhance the robustness of the detection system, we utilize extreme value theory and multilayer activation vectors to construct an unknown attack discriminator that can determine whether a network flow is an unknown attack. Comprehensive experiments on three open datasets demonstrate that the proposed intrusion detection system can quickly update and effectively identify known/unknown attacks in open vehicular networks.