AI-based intrusion detection systems for in-vehicle networks: a survey.

Rajapaksha, Sampath; Kalutarage, Harsha; Al-Kadri, M. Omar; Petrovski, Andrei; Madzudzo, Garikayi; Cheah, Madeline

Abstract

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.

Citation

RAJAPAKSHA, S., KALUTARAGE, H., AL-KADRI, M.O., PETROVSKI, A., MADZUDZO, G. and CHEAH, M. 2023. Al-based intrusion detection systems for in-vehicle networks: a survey. ACM computing survey [online], 55(11), article no. 237, pages 1-40. Available from: https://doi.org/10.1145/3570954

• Journal Article Type: Article
• Acceptance Date: Oct 25, 2022
• Online Publication Date: Feb 9, 2023
• Publication Date: Nov 30, 2023
• Deposit Date: Feb 14, 2023
• Publicly Available Date: Feb 14, 2023
• Journal: ACM computing surveys
• Print ISSN: 0360-0300
• Electronic ISSN: 1557-7341
• Publisher: Association for Computing Machinery (ACM)
• Peer Reviewed: Peer Reviewed
• Volume: 55
• Issue: 11
• Article Number: 237
• Pages: 1-40
• DOI: https://doi.org/10.1145/3570954
• Keywords: In-vehicle network; Machine learning; Automotive cybersecurity; Intrusion detection system (IDS); Controller area network (CAN)
• Public URL: https://rgu-repository.worktribe.com/output/1880289