Intrusion Detection with Support Vector Machines and Generative Models

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dc.contributor.advisorCampos, Shirleyen_US
dc.contributor.authorBaras, John S.en_US
dc.contributor.authorRabi, Mabenen_US
dc.contributor.departmentISRen_US
dc.date.accessioned2007-05-23T10:11:58Z
dc.date.available2007-05-23T10:11:58Z
dc.date.issued2002en_US
dc.description.abstractThis paper addresses the task of detecting intrusions in the form of malicious programs on a host computer system by inspecting the trace of system calls made by these programs. We use "attack-tree" type generative models for such intrusions to select features that are used by a Support Vector Machine Classifier. Our approach combines the ability of an HMM generative model to handle variable-length strings, i.e. the traces, and the non-asymptotic nature of Support Vector Machines that permits them to work well with small training sets.en_US
dc.format.extent205673 bytes
dc.format.mimetypeapplication/pdf
dc.identifier.urihttp://hdl.handle.net/1903/6265
dc.language.isoen_USen_US
dc.relation.ispartofseriesISR; TR 2002-22en_US
dc.subjectGlobal Communication Systemsen_US
dc.titleIntrusion Detection with Support Vector Machines and Generative Modelsen_US
dc.typeTechnical Reporten_US

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