Performance comparison of intrusion detection systems and application of machine learning to Snort system
- Submitting institution
-
University of Northumbria at Newcastle
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 22063234
- Type
- D - Journal article
- DOI
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10.1016/j.future.2017.10.016
- Title of journal
- Future Generation Computer Systems
- Article number
- -
- First page
- 157
- Volume
- 80
- Issue
- -
- ISSN
- 0167-739X
- Open access status
- Compliant
- Month of publication
- October
- Year of publication
- 2017
- 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
-
1
- Research group(s)
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F - Cyber Security and Network Systems (CyberNets)
- Citation count
- 37
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This paper is unique in the sense that machine learning is applied to an open source intrusion detection system at 10 Gbps traffic speed to improve the accuracy, which was a new work. I was an invited speaker for the International workshop on Machine Intelligence and Data Science (MIDS 2019), India on 31 May to 01 June 2019 (Link: http://www.mirlabs.net/mids19/speakers.php) where I spoke on the results of this paper. The paper’s idea has become a motivation for a paper, 'A Self-Adaptive Deep Learning-Based System for Anomaly Detection in 5G Networks' (IEEE Access, 2018) and various others as per paper citation.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -