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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


M. Omar Al-Kadri

Garikayi Madzudzo

Madeline Cheah


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.


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:

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
Keywords In-vehicle network; Machine learning; Automotive cybersecurity; Intrusion detection system (IDS); Controller area network (CAN)
Public URL


RAJAPAKSHA 2023 AI-based intrusion (VOR) (2.1 Mb)

Copyright Statement
© 2023 Association for Computing Machinery.

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