An Overview of Intrusion Detection Methods for In-Vehicle CAN Network of Intelligent Networked Vehicles

被引:0
|
作者
Guan Y. [1 ]
Ji H. [2 ]
Cui Z. [1 ]
Li H. [1 ]
Chen L. [1 ]
机构
[1] School of Mechanical Engineering, North China University of Science and Technology, Tangshan
[2] Hefei Innovation Research Institute, Beihang University, Hefei
来源
关键词
abnormal behavior; in-vehicle CAN network; intelligent and connected vehicle; intrusion detection system; network security;
D O I
10.19562/j.chinasae.qcgc.2023.ep.002
中图分类号
学科分类号
摘要
With the continuous integration of intelligent vehicle and vehicle networking technology,vehicles are developing towards intelligence and networking. As the complexity of in-vehicle network(e.g. CAN network)increases and the way in which vehicles are connected to the outside world increases,the cyber security risks faced by automobiles have risen dramatically. As an important barrier to protect vehicle network security,intrusion detection system can effectively detect external intrusion and abnormal vehicle behavior. Firstly,the security properties of the in-vehicle network are introduced,and the network security issues of the ICV,the vulnerability of the in-vehicle CAN network and the attack modes on it are analyzed. Secondly,the status quo of research on vehicle CAN network intrusion detection methods in recent years is summarized. Finally,several open questions are proposed for the future development of the in-vehicle network intrusion detection system. © 2023 SAE-China. All rights reserved.
引用
收藏
页码:922 / 935
页数:13
相关论文
共 63 条
  • [31] A language-based intrusion detection approach for automotive embedded networks[J], International Journal of Embedded Systems, 10, 1, pp. 1-12, (2018)
  • [32] JIN S Y, CHUNG J G, XU Y N., Signature-based intrusion detection system(IDS)for in-vehicle CAN bus network[C], IEEE International Symposium on Circuits and Systems, pp. 1-5, (2021)
  • [33] MURVAY P S,, GROZA B., Source identification using signal characteristics in controller area networks[J], IEEE Signal Processing Letters, 21, 4, pp. 395-399, (2014)
  • [34] LIU J., An experimental study towards attacker identification in automotive networks[C], 2019 IEEE Global Communications Conference, pp. 1-6, (2019)
  • [35] TIAN M Q, JIANG R B,, XING C Q,, Et al., Exploiting temperature-varied ecu fingerprints for source identification in in-vehicle network intrusion detection[C], 2019 IEEE 38th International Performance Computing and Communications Conference (IPCCC), pp. 1-8, (2019)
  • [36] ZHAO Y L, XUN Y J, LIU J J., ClockIDS:a real-time vehicle intrusion detection system based on clock skew[J], IEEE Internet of Things Journal, 9, 17, pp. 15593-15606, (2022)
  • [37] MUTER M,ASAJ N., Entropy-based anomaly detection for in-vehicle networks[C], IEEE Intelligent Vehicles Symposium, pp. 1110-1115, (2011)
  • [38] YU H, QIN G H, SUN M H,, Et al., Vehicle CAN bus network security issues and anomaly detection methods[J], Journal of Jilin University(Engineering Edition), 46, 4, pp. 1246-1253, (2016)
  • [39] MARCHETTI M, Et al., Evaluation of anomaly detection for in-vehicle networks through information-theoretic algorithms[C], IEEE International Forum on Research and Technologies for Society and Industry Leveraging a Better Tomorrow, pp. 1-6, (2016)
  • [40] Sliding window optimized information entropy analysis method for intrusion detection on in-vehicle networks[J], IEEE Access, 6, pp. 45233-45245, (2018)