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Anomaly Detection with Self-Organizing Maps and Effects of Principal Component Analysis on Feature Vectors

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dc.contributor.authorKizilören, Tevfik
dc.contributor.authorGermen, Emin
dc.contributor.orcid0000-0001-8129-6488
dc.contributor.orcid0000-0003-1301-3786
dc.date.accessioned2025-11-13T21:20:49Z
dc.date.issued2009-01-01
dc.identifier.doihttps://doi.org/10.1109/icnc.2009.652
dc.identifier.openalexW2158484931
dc.identifier.urihttps://hdl.handle.net/11421/13107
dc.identifier.urihttps://doi.org/10.1109/icnc.2009.652
dc.language.isoen
dc.rightsrestrictedAccess
dc.subjectComputer science
dc.subjectPrincipal component analysis
dc.subjectData mining
dc.subjectAnomaly detection
dc.subjectClassifier (UML)
dc.subjectArtificial intelligence
dc.subjectBenchmark (surveying)
dc.subjectIntrusion detection system
dc.subjectPattern recognition (psychology)
dc.subjectFeature extraction
dc.subjectFeature (linguistics)
dc.subjectMachine learning
dc.subject.sdg16
dc.subject.sdg10
dc.titleAnomaly Detection with Self-Organizing Maps and Effects of Principal Component Analysis on Feature Vectors
dc.typeArticle
dspace.entity.typePublication
local.authorid.openalexA5017478598
local.authorid.openalexA5043409709

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