Building an Intrusion Detection System Using a Filter-Based Feature Selection Algorithm
- Submitting institution
-
Edinburgh Napier University
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 1111647
- Type
- D - Journal article
- DOI
-
10.1109/tc.2016.2519914
- Title of journal
- IEEE Transactions on Computers
- Article number
- 7387736
- First page
- 2986
- Volume
- 65
- Issue
- 10
- ISSN
- 0018-9340
- Open access status
- Out of scope for open access requirements
- Month of publication
- January
- Year of publication
- 2016
- 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
-
3
- Research group(s)
-
-
- Citation count
- 176
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This paper proposes an effective feature selection algorithm, handling linearly and nonlinearly dependent data features, to enhance network traffic classification in big data environments. The research work presented in this paper received the National Research Award 2017 from the Research Council of the Sultanate of Oman in 2017 (https://bit.ly/3pjjEqs; https://bit.ly/2OEwp25). The ideas were adopted for touch-based biometric feature selection, which has been presented in a recent publication by a PhD student (Aaby et al. 2020 (https://doi.org/10.1109/DASC-PICom-CBDCom-CyberSciTech49142.2020.00023)).
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -