A distributed anomaly detection system for in-vehicle network using HTM

Wang, Chundong, Zhao, Zhentang, Gong, Liangyi, Zhu, Likun, Liu, Zheli and Cheng, Xiaochun ORCID logoORCID: https://orcid.org/0000-0003-0371-9646 (2018) A distributed anomaly detection system for in-vehicle network using HTM. IEEE Access, 6 . pp. 9091-9098. ISSN 2169-3536 [Article] (doi:10.1109/access.2018.2799210)

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With the development of 5G and Internet of Vehicles technology, the possibility of remote wireless attack on an in-vehicle network has been proven by security researchers. Anomaly detection technology can effectively alleviate the security threat, as the first line of security defense. Based on this, this paper proposes a distributed anomaly detection system using hierarchical temporal memory (HTM) to enhance the security of a vehicular controller area network bus. The HTM model can predict the flow data in real time, which depends on the state of the previous learning. In addition, we improved the abnormal score mechanism to evaluate the prediction. We manually synthesized field modification and replay attack in data field. Compared with recurrent neural networks and hidden Markov model detection models, the results show that the distributed anomaly detection system based on HTM networks achieves better performance in the area under receiver operating characteristic curve score, precision, and recall.

Item Type: Article
Research Areas: A. > School of Science and Technology > Computer Science
A. > School of Science and Technology > Computer Science > Artificial Intelligence group
A. > School of Science and Technology > Computer and Communications Engineering
Item ID: 24573
Notes on copyright: © 2018 IEEE. Translations and content mining are permitted for academic research only. Personal use is also permitted, but republication/redistribution requires IEEE permission. See http://www.ieee.org/publications_standards/publications/rights/index.html for more information.
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Depositing User: Xiaochun Cheng
Date Deposited: 09 Jul 2018 12:30
Last Modified: 29 Nov 2022 20:15
URI: https://eprints.mdx.ac.uk/id/eprint/24573

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