Machine Learning based intrusion detection systems for connected autonomous vehicles: A survey

Connected and Autonomous Vehicles (CAVs) expect to dramatically improve road safety and efficiency of the transportation system. However, CAVs can be vulnerable to attacks at different levels, e.g., attacks on intra-vehicle networks and inter-vehicle networks. Those malicious attacks not only result...

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Veröffentlicht in:Peer-to-peer networking and applications Jg. 16; H. 5; S. 2153 - 2185
Hauptverfasser: Nagarajan, Jay, Mansourian, Pegah, Shahid, Muhammad Anwar, Jaekel, Arunita, Saini, Ikjot, Zhang, Ning, Kneppers, Marc
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York Springer US 01.09.2023
Springer Nature B.V
Schlagworte:
Autonomous vehicles
Communications Engineering
Computer Communication Networks
Engineering
Information Systems and Communication Service
Intrusion detection systems
Machine learning
Networks
Signal,Image and Speech Processing
Traffic safety
Transportation networks
Transportation systems
Vehicles
CAN Bus
Machine learning
Security threats
Intra-vehicle network
Inter-vehicle network
Intrusion Detection System (IDS)
Connected Autonomous Vehicles (CAV)
ISSN:1936-6442, 1936-6450
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Zusammenfassung:Connected and Autonomous Vehicles (CAVs) expect to dramatically improve road safety and efficiency of the transportation system. However, CAVs can be vulnerable to attacks at different levels, e.g., attacks on intra-vehicle networks and inter-vehicle networks. Those malicious attacks not only result in loss of confidentiality and user privacy but also lead to more serious consequences such as bodily injury and loss of life. An intrusion detection system (IDS) is one of the most effective ways to monitor the operations of vehicles and networks, detect different types of attacks, and provide essential information to mitigate and remedy the effects of attacks. To ensure the safety of CAVs, it is extremely important to detect various attacks accurately in a timely fashion. The purpose of this survey is to provide a comprehensive review of available machine learning (ML) based IDS for intra-vehicle and inter-vehicle networks. Additionally, this paper discusses publicly available datasets for CAV and offers a summary of the many current testbeds and future research trends for connected vehicle environments.
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ISSN:1936-6442
1936-6450
DOI:10.1007/s12083-023-01508-7