Journal
ACM COMPUTING SURVEYS
Volume 55, Issue 11, Pages -Publisher
ASSOC COMPUTING MACHINERY
DOI: 10.1145/3570954
Keywords
Intrusion Detection System (IDS); in-vehicle network; Controller Area Network (CAN); machine learning; automotive cybersecurity
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This article surveys AI-based in-vehicle intrusion detection systems (IDSs) 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 Controller Area Network (CAN) bus, identifies the limitations of existing proposals, and gives recommendations for future research directions.
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.
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