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A LogitBoost-based algorithm for detecting known and unknown web attacks
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Kamarudin, Muhammad Hilmi, Maple, Carsten, Watson, Tim and Sohrabi Safa, Nader (2017) A LogitBoost-based algorithm for detecting known and unknown web attacks. IEEE Access, 5 . 26190 -26200. doi:10.1109/ACCESS.2017.2766844
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WRAP-LogitBoost-based-algorithm-detecting-known-unknown-web-attacks-Kamarudin-2017.pdf - Accepted Version - Requires a PDF viewer. Download (2914Kb) | Preview |
Official URL: http://dx.doi.org/10.1109/ACCESS.2017.2766844
Abstract
The rapid growth in the volume and importance of web communication throughout the Internet has heightened the need for better security protection. Security experts, when protecting systems, maintain a database featuring signatures of a large number of attacks to assist with attack detection. However, used in isolation, this can limit the capability of the system as it is only able to recognise known attacks. To overcome the problem, we propose an anomaly based intrusion detection system using an ensemble classification approach to detect unknown attacks on web servers. The process involves removing irrelevant and redundant features utilising a filter and wrapper selection procedure. Logitboost (LB) is then employed together with Random Forests (RF) as a weak classifier. The proposed ensemble technique was evaluated with some artificial datasets namely NSL-KDD, an improved version of the old KDD Cup from 1999, and the recently published UNSW-NB15 dataset. The experimental results show that our approach demonstrates superiority, in terms of accuracy and detection rate over the traditional approaches, whilst preserving low false rejection rates.
| Item Type: | Journal Article | ||||||
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| Subjects: | Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software T Technology > TK Electrical engineering. Electronics Nuclear engineering |
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| Divisions: | Faculty of Science > WMG (Formerly the Warwick Manufacturing Group) | ||||||
| Library of Congress Subject Headings (LCSH): | Web servers, Data mining, Computer security, Hacking | ||||||
| Journal or Publication Title: | IEEE Access | ||||||
| Publisher: | IEEE | ||||||
| ISSN: | 2169-3536 | ||||||
| Official Date: | 3 November 2017 | ||||||
| Dates: |
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| Volume: | 5 | ||||||
| Page Range: | 26190 -26200 | ||||||
| DOI: | 10.1109/ACCESS.2017.2766844 | ||||||
| Status: | Peer Reviewed | ||||||
| Publication Status: | Published | ||||||
| Access rights to Published version: | Open Access | ||||||
| Funder: | Engineering and Physical Sciences Research Council (EPSRC) |
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