Federated Intrusion Detection for Smart Vehicles

Cybersecurity threats endanger networked systems consisting of multiple nodes, such as the Internet of Things (IoT), Supply Chains and Smart Cars, due to the emergence of new types of unknown (zero-day) attacks against vulnerable IoT devices. While earlier research has shown that Machine Learning (ML)-based Intrusion Detection Systems (IDSs) enhance security, the efficacy and practicality of ML-based IDSs often require large training datasets based on private data. This challenge becomes more pronounced in the case of a distributed system, where each node must ensure the confidentiality of local data, yet faces attacks that are similar to those faced by other nodes in the system. Thus, this paper proposes a novel Decentralized Asynchronous IDS with Online Self-Supervised Federated Learning called the iDAF system, that improves overall security by providing collaborative confidentiality-preserving learning among all nodes. iDAF enables self-supervised learning without human intervention or large labelled data, and avoids time-consuming local training sessions. iDAF’s performance is evaluated for DoS and DDoS attacks in two different scenarios of Collaborating IoT Networks and Connected Smart Cars using three online datasets, and compared with five benchmark methods. The results show high intrusion detection accuracy with an acceptable computation time and low communication overhead.