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: 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)

Preview

PDF - Published version (with publisher's formatting) Download (5MB) | Preview

Abstract

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.

Actions (login required)

View Item

Statistics

DownloadsShow export options

Export as CSVXMLJSON

Activity Overview

6 month trend

403Downloads

6 month trend

349Hits

Additional statistics are available via IRStats2.