GVIDS: A Reliable Vehicle Intrusion Detection System Based on Generative Adversarial Network

5G and artificial intelligence greatly promote the development of intelligent and connected vehicle (ICV). However, ICV opens more ports to the outside world, making it easy for hackers to intrude controller area network (CAN) and control ICV. Therefore, many researchers design intrusion detection systems (IDSs) to detect vehicle intrusion in real-time. In this paper, we propose a highly camouflaged attack method called the same origin method execution (SOME) attack. The intrusion messages of this attack have the same characteristics as normal messages and can bypass most existing IDSs. To detect this attack, we design a reliable IDS for ICV based on a generative adversarial network (GAN) called GVIDS. It takes CAN messages as the input sample and trains the IDS model to distinguish the legality of messages. Experiments on two real vehicles show that GVIDS can detect most existing attacks, including spoofing, bus-off, masquerade, and SOME attacks. The average detection accuracy of GVIDS is 96.64%, and the average running time of each detection is only 0.18 ms. In addition, the experiment also shows that the detection performance of GVIDS is not affected by the value of identifiers in CAN messages.