A LogitBoost-based algorithm for detecting known and unknown web attacks
- Submitting institution
-
The University of Warwick
- Unit of assessment
- 12 - Engineering
- Output identifier
- 11981
- Type
- D - Journal article
- DOI
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10.1109/ACCESS.2017.2766844
- Title of journal
- IEEE Access
- Article number
- -
- First page
- 26190
- Volume
- 5
- Issue
- -
- ISSN
- 2169-3536
- Open access status
- Compliant
- Month of publication
- November
- 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
-
3
- Research group(s)
-
-
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This paper advances the state of the art in an area of international importance, namely intrusion detection systems. The novel approach allows a more efficient system to identify unknown attacks on web servers. The paper's rigour is demonstrated through the use of respected data sets namely NSL-KDD and the recently published UNSW-NB15 data set utilised by the Cyber Range Lab of the Australian Centre for Cyber Security (ACCS). The detection rate and FAR performance of the new algorithm exceeded that of those presented in 10 leading papers over NSL-KDD and offered significant improvement over 5 methods applied to UNSW-NB15.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -