Symmetry degree measurement and its applications to anomaly detection
- Submitting institution
-
University of the West of England, Bristol
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 847550
- Type
- D - Journal article
- DOI
-
10.1109/TIFS.2019.2933731
- Title of journal
- IEEE Transactions on Information Forensics and Security
- Article number
- -
- First page
- 1040
- Volume
- 15
- Issue
- -
- ISSN
- 1556-6013
- Open access status
- Compliant
- Month of publication
- August
- Year of publication
- 2019
- URL
-
https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=10206
- 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
- 0
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This work presented our latest research findings on anomaly detection that can identify network unusual behaviour patterns and keep the network under control. This work has been demonstrated has very good performances in the largest research networks CERNET (China Education and Research Network) in the world. The finding may have significant impact on the anomaly detection in complicated networks, such as Internet of Things (IoT), which connects billions of smart devices.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
