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

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 Sy...

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Bibliographic Details
Published in:ACM computing surveys 2023-11, Vol.55 (11), p.1-40, Article 237
Main Authors: Rajapaksha, Sampath, Kalutarage, Harsha, Al-Kadri, M. Omar, Petrovski, Andrei, Madzudzo, Garikayi, Cheah, Madeline
Format: Article
Language:English
Subjects:
Artificial intelligence
Computer science
Controller area network
Cybersecurity
In vehicle
Intrusion detection systems
Network security
Security and privacy
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Summary: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.
ISSN:0360-0300
1557-7341
DOI:10.1145/3570954