ANALYSIS OF A SEMI-SUPERVISED LEARNING APPROACH TO INTRUSION DETECTION

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dc.contributor.advisorCukier, Michelen_US
dc.contributor.authorKlimkowski, Benjamin Harolden_US
dc.contributor.departmentComputer Scienceen_US
dc.contributor.publisherDigital Repository at the University of Marylanden_US
dc.contributor.publisherUniversity of Maryland (College Park, Md.)en_US
dc.date.accessioned2014-06-24T06:17:29Z
dc.date.available2014-06-24T06:17:29Z
dc.date.issued2014en_US
dc.description.abstractThis thesis addresses the use of a semi-supervised learning (SSL) method in an intrusion detection setting. Specifically, this thesis illustrates the potential benefits and difficulties of using a cluster-then-label (CTL) SSL approach to classify stealth scanning in network flow metadata. A series of controlled tests were performed to show that, in certain situations, a CTL SSL approach could perform comparable to a supervised learner with a fraction of the development effort. This study also balances these findings with pragmatic issues like labeling, noise and feature encoding. While CTL demonstrated accuracy, research is still needed before practical implementations are a reality. The contributions of this work are 1) one of the first studies in the application of SSL in intrusion detection, illustrating the challenges of applying a CTL approach to domain with imbalanced class distributions; 2) the creation of a new intrusion detection dataset; 3) validation of previously established techniquesen_US
dc.identifier.urihttp://hdl.handle.net/1903/15393
dc.language.isoenen_US
dc.subject.pqcontrolledComputer scienceen_US
dc.subject.pqcontrolledArtificial intelligenceen_US
dc.subject.pqcontrolledJournalismen_US
dc.subject.pquncontrolledIntrusion Detectionen_US
dc.subject.pquncontrolledMachine learningen_US
dc.subject.pquncontrolledSemi-superviseden_US
dc.titleANALYSIS OF A SEMI-SUPERVISED LEARNING APPROACH TO INTRUSION DETECTIONen_US
dc.typeThesisen_US

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