Intrusion Detection Using Cost-Sensitive Classification

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dc.creatorMitrokotsa, Aikaterini
dc.creatorDimitrakakis, Christos
dc.creatorDouligeris, Christos
dc.date2008-07-13
dc.date.accessioned2026-07-07T09:50:04Z
dc.date.available2026-07-07T09:50:04Z
dc.descriptionIntrusion Detection is an invaluable part of computer networks defense. An important consideration is the fact that raising false alarms carries a significantly lower cost than not detecting at- tacks. For this reason, we examine how cost-sensitive classification methods can be used in Intrusion Detection systems. The performance of the approach is evaluated under different experimental conditions, cost matrices and different classification models, in terms of expected cost, as well as detection and false alarm rates. We find that even under unfavourable conditions, cost-sensitive classification can improve performance significantly, if only slightly.
dc.description13 pages, 6 figures, presented at EC2ND 2007
dc.identifierhttps://arxiv.org/abs/0807.2043
dc.identifierhttp://arxiv.org/abs/0807.2043
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/164818
dc.subjectCryptography and Security
dc.subjectComputer Vision and Pattern Recognition
dc.subjectNetworking and Internet Architecture
dc.titleIntrusion Detection Using Cost-Sensitive Classification
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