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

• Published in: IEEE transactions on network science and engineering Vol. 8; no. 2; pp. 1456 - 1466
• Main Authors: Javed, Abdul Rehman, Rehman, Saif ur, Khan, Mohib Ullah, Alazab, Mamoun, G, Thippa Reddy
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.04.2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Artificial neural networks; Control equipment; Controller area network; Controllers; cyberattacks; Electronic control; Electronic mail; in - vehicle network (IVN) security; In vehicle; Intrusion; Intrusion detection; Logic gates; Malfunctions; Performance evaluation; Protocols; Security; security protocols; Testing
• ISSN: 2327-4697; 2334-329X
• DOI: 10.1109/TNSE.2021.3059881

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.