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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Vydáno v:	Peer-to-peer networking and applications Ročník 16; číslo 5; s. 2153 - 2185
Hlavní autoři:	Nagarajan, Jay, Mansourian, Pegah, Shahid, Muhammad Anwar, Jaekel, Arunita, Saini, Ikjot, Zhang, Ning, Kneppers, Marc
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	New York Springer US 01.09.2023 Springer Nature B.V
Témata:	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
On-line přístup:	Získat plný text
Tagy:	Přidat tag Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!

Abstract	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.
AbstractList	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.
Author	Mansourian, Pegah Saini, Ikjot Nagarajan, Jay Kneppers, Marc Shahid, Muhammad Anwar Jaekel, Arunita Zhang, Ning
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Snippet	Connected and Autonomous Vehicles (CAVs) expect to dramatically improve road safety and efficiency of the transportation system. However, CAVs can be...
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SubjectTerms	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
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Title	Machine Learning based intrusion detection systems for connected autonomous vehicles: A survey
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