SAMPATH RAJAPAKSHA R WASALA MUDIYANSELAGE POLWATTE GEDARA s.rajapaksha@rgu.ac.uk
Research Student
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
Authors
Dr Harsha Kalutarage h.kalutarage@rgu.ac.uk
Lecturer
M. Omar Al-Kadri
Andrei Petrovski
Garikayi Madzudzo
Madeline Cheah
Abstract
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.
Citation
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: https://doi.org/10.1145/3570954
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 | ACM Association for Computing Machinery |
Peer Reviewed | Peer Reviewed |
Volume | 55 |
Issue | 11 |
Article Number | 237 |
Pages | 1-40 |
DOI | https://doi.org/10.1145/3570954 |
Keywords | In-vehicle network; Machine learning; Automotive cybersecurity; Intrusion detection system (IDS); Controller area network (CAN) |
Public URL | https://rgu-repository.worktribe.com/output/1880289 |
Files
RAJAPAKSHA 2023 AI-based intrusion (VOR)
(2.1 Mb)
PDF
Copyright Statement
© 2023 Association for Computing Machinery.
You might also like
Keep the moving vehicle secure: context-aware intrusion detection system for in-vehicle CAN bus security.
(2022)
Conference Proceeding
Advanced persistent threats detection based on deep learning approach.
(2023)
Conference Proceeding
Android source code vulnerability detection: a systematic literature review.
(2023)
Journal Article
Topology for preserving feature correlation in tabular synthetic data.
(2022)
Conference Proceeding
Framework for detecting APTs based on steps analysis and correlation.
(2022)
Book Chapter