CANintelliIDS: Detecting In-Vehicle Intrusion Attacks on a Controller Area Network using CNN and Attention-based GRU

Controller Area Network (CAN) is a communication protocol that provides reliable and productive transmission between invehicle nodes continuously. CAN bus protocol is broadly utilized standard channel to deliver sequential communications between Electronic Control Units (ECUs) due to simple and reliable invehicle communication. Existing studies report how easily an attack can be performed on the CAN bus of in-vehicle due to weak security mechanisms that could lead to system malfunctions. Hence the security of communications inside a vehicle is a latent problem. In this paper, we propose a novel approach named CANintelliIDS, for vehicle intrusion attack detection on the CAN bus. CANintelliIDS is based on a combination of CNN and attention-based GRU model to detect single intrusion attacks as well as mixed intrusion attacks on a CAN bus. The proposed CANintelliIDS model is evaluated extensively and it achieved a performance gain of 10.79% on test intrusion attacks over existing approaches.