A novel method for unsupervised anomaly detection using unlabelled data

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dc.contributor.authorAbdul Samad Bin Haji Ismail
dc.contributor.authorAbdul Hanan Abdullah
dc.contributor.authorKamalrulnizam Bin Abu Bak
dc.contributor.authorMd Asri Bin Ngadi
dc.contributor.authorDahliyusmanto Dahlan
dc.contributor.authorWitcha Chimphlee
dc.contributor.correspondenceA. S. B. H. Ismail; Faculty of Science and Information Systems, Universiti Teknologi Malaysia, 81310 Skudai, Johor, Malaysia; email: abdsamad@utm.my
dc.date.accessioned2025-03-10T07:38:07Z
dc.date.available2025-03-10T07:38:07Z
dc.date.issued2008
dc.description.abstractMost current intrusion detection methods cannot process large amounts of audit data for real-time operation. In this paper, anomaly network intrusion detection method based on Principal Component Analysis (PCA) for data reduction and Fuzzy Adaptive Resonance Theory (Fuzzy ART) for classifier is presented. Moreover, PCA is applied to reduce the high dimensional data vectors and distance between a vector and its projection onto the subspace reduced is used for anomaly detection. Using a set of benchmark data from KDD (Knowledge Discovery and Data Mining) Competition designed by DARPA for demonstrate to detection intrusions. Experimental results show the proposed model can classify the network connections with satisfying performance. © 2008 IEEE.
dc.identifier.citationProceedings - The International Conference on Computational Sciences and its Applications, ICCSA 2008
dc.identifier.doi10.1109/ICCSA.2008.70
dc.identifier.isbn978-076953243-1
dc.identifier.scopus2-s2.0-52449120181
dc.identifier.urihttps://repository.dusit.ac.th//handle/123456789/5050
dc.languageEnglish
dc.rights.holderScopus
dc.subjectAnomaly detection
dc.subjectClustering
dc.subjectIntrusion detection
dc.subjectNetwork security
dc.subjectPrincipal component analysis
dc.subjectUnsupervised learning
dc.titleA novel method for unsupervised anomaly detection using unlabelled data
dc.typeConference paper
mods.location.urlhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-52449120181&doi=10.1109%2fICCSA.2008.70&partnerID=40&md5=dfb6d24f98455ca483b5655b30dc1a70
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oaire.citation.startPage252
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