Web attack detection using entropy-based analysis

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dc.contributor.authorThreepak, T.
dc.contributor.authorWatcharapupong, A.
dc.date.accessioned2026-08-06T10:09:12Z
dc.date.available2026-08-06T10:09:12Z
dc.date.issued2014-01-01
dc.description.abstractWeb attacks are increases both magnitude and complexity. In this paper, we try to use the Shannon entropy analysis to detect these attacks. Our approach examines web access logging text using the principle that web attacking scripts usually have more sophisticated request patterns than legitimate ones. Risk level of attacking incidents are indicated by the average (AVG) and standard deviation (SD) of each entropy period, i.e., Alpha and Beta lines which are equal to AVG-SD and AVG-2*SD, respectively. They represent boundaries in detection scheme. As the result, our technique is not only used as high accurate procedure to investigate web request anomaly behaviors, but also useful to prune huge application access log files and focus on potential intrusive events. The experiments show that our proposed process can detect anomaly requests in web application system with proper effectiveness and low false alarm rate. © 2014 IEEE.
dc.identifier.citationInternational Conference on Information Networking, 244-247, 2014
dc.identifier.doi10.1109/ICOIN.2014.6799699
dc.identifier.issn19767684
dc.identifier.other2-s2.0-84899925546
dc.identifier.urihttps://dspace.kmitl.ac.th/handle/123456789/5586
dc.sourceInternational Conference on Information Networking
dc.subjectAnomaly Detection
dc.subjectEntropy Analysis
dc.subjectInformation Security
dc.titleWeb attack detection using entropy-based analysis
dc.typeConference Paper

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