GPIDS: GAN Assisted Contextual Pattern-Aware Intrusion Detection System for IVN

The intelligent connected vehicle (ICV) has garnered considerable attention in recent years due to developments in vehicle-to-everything (V2X) technology, 5G communication networks, and more. However, the connection between the in-vehicle network (IVN) and external network exposes vehicles to potential intrusion risks. In particular, the controller area network (CAN) protocol, a typical IVN responsible for electronic control unit cooperation, lacks defense mechanisms like encryption or authentication, further making vehicles vulnerable to intrusion. Therefore, many scholars propose countermeasures to address the weakness of CAN, namely message authentication and intrusion detection systems (IDS). Given that the former may occupy extra bandwidth and computational resources, we prioritize IDS in this paper. Thus, we propose a generative adversarial network assisted contextual pattern-aware IDS (GPIDS) against several typical vehicle attacks, including bus-off, spoofing, masquerade, replay, fuzzy, and same origin method execution (SOME). The SOME attack stems from the Internet of Things field and possesses high disguise property, which can mimic physical features as normal messages in IVN, like clock skew, traffic, voltage, and so on. Notably, to the best of our knowledge, we are the first to present an IDS capable of effectively addressing SOME attacks. Extensive experiments have been conducted on four real vehicles, demonstrating that GPIDS can accurately detect the aforementioned attacks with low latency.