A LogitBoost-Based Algorithm for Detecting Known and Unknown Web Attacks
- Submitting institution
-
Oxford Brookes University
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 185750300
- 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
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3
- Research group(s)
-
-
- Citation count
- 15
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This paper describes a novel approach for detecting known and unknown web attacks through the use of ensemble machine learning algorithms. This is believed to be the first approach to investigate the use of boosting algorithm to improve the detection on web attacks. This work has led to a significant improvement in the known and unknown attack detection rate while reducing the false alarm rate. This research is now informing the work of two current PhD projects on security in autonomous vehicles.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -