Abstract
With increased wireless connectivity and embedded sensors, vehicles are becoming more intelligent, offering Internet access, telematics, and advanced driver assistance systems. Along with all benefits, connectivity to the public network and automotive control systems introduces new threats and security risks to connected and autonomous driving systems. Therefore, it is highly critical to design robust security mechanisms to protect the system from potential attacks and security vulnerabilities. An intrusion detection system (IDS) is a promising solution to detect and identify attacks and malicious behaviour within the network. This paper proposes a two-layer IDS mechanism that exploits machine learning (ML) solutions for collaborative attack detection between an on-vehicle IDS module and a developed IDS platform at a mobile edge computing (MEC) server. The results illustrate that the proposed solution can significantly reduce communication latency and energy consumption up to 80% while maintaining a high level of detection accuracy.