A Deep Learning Approach to Network Intrusion Detection
- Submitting institution
-
Liverpool John Moores University
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 923
- Type
- D - Journal article
- DOI
-
10.1109/TETCI.2017.2772792
- Title of journal
- IEEE Transactions on Emerging Topics in Computational Intelligence
- Article number
- -
- First page
- 41
- Volume
- 2
- Issue
- 1
- ISSN
- 2471-285X
- Open access status
- Compliant
- Month of publication
- January
- Year of publication
- 2018
- 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
- -
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This work is a key output from a collaborative research project undertaken by LJMU and LQDTU in Vietnam as part of the RAE-funded Newton Research Collaboration Programme in 2017 (NRCP1617/6/214). It presents a deep learning-based technique for network intrusion detection, which offers significant accuracy improvements. The evaluation conducted using a renowned (within the field) benchmark dataset has confirmed these improvements over related work. Since its publication, the paper has attracted significant attention with 8400+ views and been widely referenced, e.g. in IEEE TIFS, TII and TFS (https://ieeexplore.ieee.org/document/8264962).
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
