AI-Based Intrusion Detection Systems for In-Vehicle Networks: A Survey

• Published in: ACM computing surveys Vol. 55; no. 11; pp. 1 - 40
• Main Authors: Rajapaksha, Sampath, Kalutarage, Harsha, Al-Kadri, M. Omar, Petrovski, Andrei, Madzudzo, Garikayi, Cheah, Madeline
• Format: Journal Article
• Language: English
• Published: New York, NY ACM 30.11.2023; Association for Computing Machinery
• Subjects: Artificial intelligence; Computer science; Controller area network; Cybersecurity; In vehicle; Intrusion detection systems; Network security; Security and privacy; Taxonomy; Controller Area Network (CAN); automotive cybersecurity; machine learning; in-vehicle network; Intrusion Detection System (IDS)
• ISSN: 0360-0300; 1557-7341
• DOI: 10.1145/3570954

The Controller Area Network (CAN) is the most widely used in-vehicle communication protocol, which still lacks the implementation of suitable security mechanisms such as message authentication and encryption. This makes the CAN bus vulnerable to numerous cyber attacks. Various Intrusion Detection Systems (IDSs) have been developed to detect these attacks. However, the high generalization capabilities of Artificial Intelligence (AI) make AI-based IDS an excellent countermeasure against automotive cyber attacks. This article surveys AI-based in-vehicle IDS from 2016 to 2022 (August) with a novel taxonomy. It reviews the detection techniques, attack types, features, and benchmark datasets. Furthermore, the article discusses the security of AI models, necessary steps to develop AI-based IDSs in the CAN bus, identifies the limitations of existing proposals, and gives recommendations for future research directions.