Distributed anomaly detection using minimum volume elliptical principal component analysis
- Submitting institution
-
University of Glasgow
- Unit of assessment
- 12 - Engineering
- Output identifier
- 12-04732
- Type
- D - Journal article
- DOI
-
10.1109/TKDE.2016.2555804
- Title of journal
- IEEE Transactions on Knowledge and Data Engineering
- Article number
- -
- First page
- 2320
- Volume
- 28
- Issue
- 9
- ISSN
- 1041-4347
- Open access status
- Compliant
- Month of publication
- September
- Year of publication
- 2016
- URL
-
http://eprints.gla.ac.uk/132543/
- 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
-
2
- Research group(s)
-
-
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This paper provides solution to detect anomaly in a dynamic and continuously changing data set (e.g. real-time data collected from IoT devices and cellular network performance indicators). The results underpinned further development of Self Organised Networking principles leading to grants NSF USA $250k NSF#1559483, and Scotland 5G Centre £8M https://scotland5gcentre.org.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
