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dc.contributor.authorKamarudin, Muhammad Hilmi
dc.contributor.authorMaple, Carsten
dc.contributor.authorWatson, Tim
dc.contributor.authorSafa, Nader Sohrabi
dc.date.accessioned2021-06-18T12:54:25Z
dc.date.available2021-06-18T12:54:25Z
dc.date.issued2017-11-03
dc.identifier.citationM. H. Kamarudin, C. Maple, T. Watson and N. S. Safa, "A LogitBoost-Based Algorithm for Detecting Known and Unknown Web Attacks," in IEEE Access, vol. 5, pp. 26190-26200, 2017, doi: 10.1109/ACCESS.2017.2766844.en
dc.identifier.issn2169-3536en
dc.identifier.doi10.1109/access.2017.2766844en
dc.identifier.urihttp://hdl.handle.net/2436/624129
dc.description© 2017 The Authors. Published by IEEE. This is an open access article available under a Creative Commons licence. The published version can be accessed at the following link on the publisher’s website: https://doi.org/10.1109/ACCESS.2017.2766844en
dc.description.abstractThe 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 recognize 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 is then employed together with random forests as a weak classifier. The proposed ensemble technique was evaluated with some artificial data sets namely NSL-KDD, an improved version of the old KDD Cup from 1999, and the recently published UNSW-NB15 data set. 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.en
dc.formatapplication/pdfen
dc.language.isoenen
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)en
dc.relation.urlhttps://ieeexplore.ieee.org/document/8094857en
dc.subjectanomaly detectionen
dc.subjectintrusion detectionen
dc.subjectdata miningen
dc.subjectclassificationen
dc.subjectweb attacksen
dc.titleA LogitBoost-based algorithm for detecting known and unknown web attacksen
dc.typeJournal articleen
dc.identifier.eissn2169-3536
dc.identifier.journalIEEE Accessen
dc.date.updated2021-06-15T07:16:27Z
dc.date.accepted2017-10-05
rioxxterms.funderThe University of Wolverhamptonen
rioxxterms.identifier.projectUOW18062021NSSen
rioxxterms.versionVoRen
rioxxterms.licenseref.urihttp://creativecommons.org/licenses/by/4.0/en
rioxxterms.licenseref.startdate2021-06-18en
dc.source.volume5
dc.source.volume5
dc.source.beginpage26190
dc.source.beginpage26190
dc.source.endpage26200
dc.source.endpage26200
dc.description.versionPublished version
refterms.dateFCD2021-06-18T12:51:38Z
refterms.versionFCDAM
refterms.dateFOA2021-06-18T00:00:00Z


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