Intrusion detection via independent component analysis based on rough fuzzy

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dc.contributor.authorSurat Srinoy
dc.contributor.authorWerasak Kurutach
dc.contributor.authorWitcha Chimphlee
dc.contributor.authorSiriporn Chimphlee
dc.contributor.authorSanti Sounsri
dc.contributor.correspondenceS. Srinoy; Department of Computer Engineering, Mahanakorn University of Technology, Nongchok, Bangkok, 51 Chuemsumphun Road, Thailand; email: surat_sri@dusit.ac.th
dc.date.accessioned2025-03-10T07:38:08Z
dc.date.available2025-03-10T07:38:08Z
dc.date.issued2006
dc.description.abstractIndependent component analysis (ICA) aims at extracting unknown hidden factors/components from multivariate data using only the assumption that unknown factors are mutually independent. In this paper we discuss an intrusion detection method that proposes independent component analysis based feature selection heuristics and using rough fuzzy for clustering data. Rough set has to decrease the amount of data and get rid of redundancy and Fuzzy methods allow objects to belong to several clusters simultaneously, with different degrees of membership. The experimental results on Knowledge Discovery and Data Mining-(KDDCup 1999) dataset.
dc.identifier.citationWSEAS Transactions on Computers
dc.identifier.issn11092750
dc.identifier.scopus2-s2.0-30644466563
dc.identifier.urihttps://repository.dusit.ac.th//handle/123456789/5066
dc.languageEnglish
dc.rights.holderScopus
dc.subjectAnomaly detection
dc.subjectIndependent component analysis
dc.subjectIntrusion detection system
dc.subjectRough fuzzy
dc.titleIntrusion detection via independent component analysis based on rough fuzzy
dc.typeArticle
mods.location.urlhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-30644466563&partnerID=40&md5=894faae60a607a55e41e0c0aa6e09b86
oaire.citation.endPage48
oaire.citation.issue1
oaire.citation.startPage43
oaire.citation.volume5
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