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Intrusion detection in unlabeled data with quarter-sphere support vector machines

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dc.contributor.authorLaskov, Pavel
dc.contributor.authorChristin, Schäfer
dc.contributor.authorKotenko, Igor
dc.contributor.editorFlegel, Ulrich
dc.contributor.editorMeier, Michael
dc.date.accessioned2019-10-16T08:50:51Z
dc.date.available2019-10-16T08:50:51Z
dc.date.issued2004
dc.description.abstractPractical application of data mining and machine learning techniques to intrusion detection is often hindered by the difficulty to produce clean data for the training. To address this problem a geometric framework for unsupervised anomaly detection has been recently proposed. In this framework, the data is mapped into a feature space, and anomalies are detected as the entries in sparsely populated regions. In this contribution we propose a novel formulation of a one-class Support Vector Machine (SVM) specially designed for typical IDS data features. The key idea of our "quarter-sphere" algorithm is to encompass the data with a hypersphere anchored at the center of mass of the data in feature space. The proposed method and its behavior on varying percentages of attacks in the data is evaluated on the KDDCup 1999 dataset.en
dc.identifier.isbn3-88579-375-X
dc.identifier.pissn1617-5468
dc.identifier.urihttps://dl.gi.de/handle/20.500.12116/29207
dc.language.isoen
dc.publisherGesellschaft für Informatik e.V.
dc.relation.ispartofDetection of intrusions and malware & vulnerability assessment, GI SIG SIDAR workshop, DIMVA 2004
dc.relation.ispartofseriesLecture Notes in Informatics (LNI) - Proceedings, Volume P-46
dc.titleIntrusion detection in unlabeled data with quarter-sphere support vector machinesen
dc.typeText/Conference Paper
gi.citation.endPage82
gi.citation.publisherPlaceBonn
gi.citation.startPage71
gi.conference.dateJuly 6-7, 2004
gi.conference.locationDortmund
gi.conference.sessiontitleRegular Research Papers

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P046 - DIMVA 2004 - Detection of intrusions and malware & vulnerability assessment, GI SIG SIDAR workshop,