Anomaly-based intrusion detection using fuzzy rough clustering

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dc.contributor.authorWitcha Chimphlee
dc.contributor.authorAbdul Hanan Abdullah
dc.contributor.authorMohd Noor Md. Sap
dc.contributor.authorSurat Srinoy
dc.contributor.authorSiriporn Chimphlee
dc.contributor.correspondenceW. Chimphlee; Faculty of Science and Technology, Suan Dusit Rajabhat University, Thailand; email: witcha_chi@dusit.ac.th
dc.date.accessioned2025-03-10T07:38:08Z
dc.date.available2025-03-10T07:38:08Z
dc.date.issued2006
dc.description.abstractIt is an important issue for the security of network to detect new intrusion attack and also to increase the detection rates and reduce false positive rates in Intrusion Detection System (IDS). Anomaly intrusion detection focuses on modeling normal behaviors and identifying significant deviations, which could be novel attacks. The normal and the suspicious behavior in computer networks are hard to predict as the boundaries between them cannot be well defined. We apply the idea of the Fuzzy Rough C-means (FRCM) to clustering analysis. FRCM integrates the advantage of fuzzy set theory and rough set theory that the improved algorithm to network intrusion detection. The experimental results on dataset KDDCup99 show that our method outperforms the existing unsupervised intrusion detection methods © 2006 IEEE.
dc.identifier.citationProceedings - 2006 International Conference on Hybrid Information Technology, ICHIT 2006
dc.identifier.doi10.1109/ICHIT.2006.253508
dc.identifier.isbn0769526748; 978-076952674-4
dc.identifier.scopus2-s2.0-34247263845
dc.identifier.urihttps://repository.dusit.ac.th//handle/123456789/5076
dc.languageEnglish
dc.rights.holderScopus
dc.titleAnomaly-based intrusion detection using fuzzy rough clustering
dc.typeConference paper
mods.location.urlhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-34247263845&doi=10.1109%2fICHIT.2006.253508&partnerID=40&md5=d0866cb1bc67c95f8ebd123cd4619e47
oaire.citation.endPage334
oaire.citation.startPage329
oaire.citation.volume1
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