An efficient feature selection based Bayesian and rough set approach for intrusion detection
- Submitting institution
-
University of the West of Scotland
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 21434611
- Type
- D - Journal article
- DOI
-
10.1016/j.asoc.2019.105980
- Title of journal
- Applied Soft Computing
- Article number
- 105980
- First page
- -
- Volume
- 87
- Issue
- -
- ISSN
- 1568-4946
- Open access status
- Compliant
- Month of publication
- December
- Year of publication
- 2019
- URL
-
-
- 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)
-
-
- Citation count
- 11
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This is outcome from joint-collaborative research between UWS and Indian Institute of Technology(IIT) to study network intrusion behaviours and detection funded by IIT/Indian Government. The paper was finalised when research collaborator from IIT visited the AVCN research centre UWS in June 2019. A feature selection proposed to compute core features and ranked them based on estimated probability for high dimensional datasets of intrusion detection systems. Uncertain information is distinguished using rough set approximations and solved by the Bayes theorem. This paper follows previous original article presented at IEEE-ICCNT2019 under this joint-collaboration, and leads to invitation for keynote talk at ICIA-2020(http://www.cianitjsr.in/).
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
