On-line Adaptive IDS Scheme for Detecting Unknown Network Attacks using HMM Models

dc.contributor.advisorBaras, John S.en_US
dc.contributor.authorBojanic, Irenaen_US
dc.contributor.departmentISRen_US
dc.contributor.departmentSEILen_US
dc.date.accessioned2007-05-23T10:17:48Z
dc.date.available2007-05-23T10:17:48Z
dc.date.issued2005en_US
dc.description.abstractAn important problem in designing IDS schemes is an optimal trade-off between good detection and false alarm rate. Specifically, in order to detect unknown network attacks, existing IDS schemes use anomaly detection which introduces a high false alarm rate. In this thesis we propose an IDS scheme based on overall behavior of the network. We capture the behavior with probabilistic models (HMM) and use only limited logic information about attacks. Once we set the detection rate to be high, we filter out false positives through stages. The key idea is to use probabilistic models so that even an unknown attack can be detected, as well as a variation of a previously known attack. The scheme is adaptive and real-time.en_US
dc.format.extent1497834 bytes
dc.format.mimetypeapplication/pdf
dc.identifier.urihttp://hdl.handle.net/1903/6550
dc.language.isoen_USen_US
dc.relation.ispartofseriesISR; MS 2005-1en_US
dc.relation.ispartofseriesSEIL; MS 2005-1en_US
dc.subjectNetwork Securityen_US
dc.titleOn-line Adaptive IDS Scheme for Detecting Unknown Network Attacks using HMM Modelsen_US
dc.typeThesisen_US

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