En-ABC: An ensemble artificial bee colony based anomaly detection scheme for cloud environment
- Submitting institution
-
University of Newcastle upon Tyne
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 261296-84839-1292
- Type
- D - Journal article
- DOI
-
10.1016/j.jpdc.2019.09.013
- Title of journal
- Journal of Parallel and Distributed Computing
- Article number
- -
- First page
- 219
- Volume
- 135
- Issue
- -
- ISSN
- 0743-7315
- Open access status
- Not compliant
- Month of publication
- September
- Year of publication
- 2019
- URL
-
https://doi.org/10.1016/j.jpdc.2019.09.013
- 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
-
7
- Research group(s)
-
F - Networked and Ubiquitous Systems Engineering (NUSE)
- Citation count
- 17
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This is the first paper to propose a generalised solution to identifying malicious behaviour within cloud environments across a wide domain of application types (previously a solution would only be valid for a particular application domain, e.g., streaming services). Our approach utilises an AI based algorithm that can learn and adapt to multi-application domain network traffic in real-time to identify traffic patterns that are anomalous for each application (potentially malicious). The work – a result of international collaboration - is industry benchmarked and shows our approach achieves better detection rates than domain specialised approaches.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -