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

Abstract

Controller area network (CAN) is a communication protocol that provides reliable and productive transmission between in-vehicle nodes continuously. CAN bus protocol is broadly utilized standard channel to deliver sequential communications between electronic control units (ECUs) due to simple and reliable in-vehicle 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 convolutional neural network (CNN) and attention-based gated recurrent unit (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.