A distributed anomaly detection system for in-vehicle network using HTM
- Submitting institution
-
Middlesex University
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 914
- Type
- D - Journal article
- DOI
-
10.1109/access.2018.2799210
- Title of journal
- IEEE Access
- Article number
- -
- First page
- 9091
- Volume
- 6
- Issue
- -
- ISSN
- 2169-3536
- Open access status
- Compliant
- Month of publication
- January
- Year of publication
- 2018
- URL
-
http://eprints.mdx.ac.uk/24573/
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
-
5
- Research group(s)
-
-
- Citation count
- 32
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- Anomaly detection technology can effectively alleviate the threat of a remote wireless attack on an in-vehicle network. This paper proposes a distributed anomaly detection system using hierarchical temporal memory (HTM). The HTM model can predict data flow in real time. The paper also improved the abnormal score mechanism to evaluate the prediction, and considered field modification and replay attack in data field. This paper is significant because the distributed anomaly detection system based on HTM networks achieves better performance than systems based on recurrent neural networks or on hidden Markov model detection models.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -