Intrusion Detection in Unlabeled Data with Quarter-sphere Support Vector Machines

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dc.contributor.authorLaskov, Pavel
dc.contributor.authorSchäfer, Christin
dc.contributor.authorKotenko, Igor
dc.contributor.authorMüller, Klaus-Robert
dc.date.accessioned2018-10-15T11:12:06Z
dc.date.available2018-10-15T11:12:06Z
dc.date.issued2004
dc.descriptionDieser Beitrag ist mit Zustimmung des Rechteinhabers aufgrund einer (DFG geförderten) Allianz- bzw. Nationallizenz frei zugänglich.de
dc.descriptionThis publication is with permission of the rights owner freely accessible due to an Alliance licence and a national licence (funded by the DFG, German Research Foundation) respectively.en
dc.description.abstractThe anomaly detection methods are receiving growing attention in the intrusion detection community. The two main reasons for this are their ability to handle large volumes of unlabeled data and to detect previously unknown attacks. In this contribution we investigate the application of a modern machine learning technique – one-class Support Vector Machines (SVM) – for anomaly detection in unlabeled data. We propose a novel formulation of this technique which is particularly suited for the data typical for intrusion detection systems. Our evaluation on the well-known KDDCup dataset demonstrates a significant improvement over previous formulations of the one-class SVM.en
dc.description.sponsorshipBMBF, 01-SC40A, MINDen
dc.description.sponsorshipEC/FP6/506778/EU/Pattern analysis, statistical modelling and computational Learning/PASCALen
dc.identifier.eissn1865-8342
dc.identifier.issn0930-5157
dc.identifier.urihttps://depositonce.tu-berlin.de/handle/11303/8355
dc.identifier.urihttp://dx.doi.org/10.14279/depositonce-7507
dc.language.isoen
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/
dc.subject.ddc004 Datenverarbeitung; Informatikde
dc.subject.othermachine learningen
dc.subject.otherSVMen
dc.subject.otheranomalyen
dc.subject.otherKDDCup dataseten
dc.titleIntrusion Detection in Unlabeled Data with Quarter-sphere Support Vector Machinesen
dc.typeArticleen
dc.type.versionpublishedVersionen
dcterms.bibliographicCitation.doi10.1515/PIKO.2004.228
dcterms.bibliographicCitation.issue4
dcterms.bibliographicCitation.journaltitlePraxis der Informationsverarbeitung und Kommunikation : PIKde
dcterms.bibliographicCitation.originalpublishernameDe Gruyteren
dcterms.bibliographicCitation.originalpublisherplaceBerlinen
dcterms.bibliographicCitation.pageend236
dcterms.bibliographicCitation.pagestart228
dcterms.bibliographicCitation.volume27
tub.accessrights.dnbdomain
tub.affiliationFak. 4 Elektrotechnik und Informatik::Inst. Softwaretechnik und Theoretische Informatik::FG Maschinelles Lernende
tub.affiliation.facultyFak. 4 Elektrotechnik und Informatikde
tub.affiliation.groupFG Maschinelles Lernende
tub.affiliation.instituteInst. Softwaretechnik und Theoretische Informatikde
tub.publisher.universityorinstitutionTechnische Universität Berlinde

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