Learning Symbolic User Models for Intrusion Detection: A Method and Initial Results

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dc.contributor.authorMichalski, Ryszard S.
dc.contributor.authorKaufman, Kenneth A.
dc.contributor.authorPietrzykowski, Jaroslaw
dc.contributor.authorŚnieżyński, Bartłomiej
dc.contributor.authorWojtusiak, Janusz
dc.date.accessioned2006-11-03T18:17:37Z
dc.date.available2006-11-03T18:17:37Z
dc.date.issued2006-06
dc.description.abstractThis paper briefly describes the LUS-MT method for automatically learning user signatures (models of computer users) from datastreams capturing users’ interactions with computers. The signatures are in the form of collections of multistate templates (MTs), each characterizing a pattern in the user’s behavior. By applying the models to new user activities, the system can detect an imposter or verify legitimate user activity. Advantages of the method include the high expressive power of the models (a single template can characterize a large number of different user behaviors) and the ease of their interpretation, which makes possible their editing or enhancement by an expert. Initial results are very promising and show the potential of the method for user modeling.
dc.description.sponsorshipThis research was supported in part by the UMCB/LUCITE #32 grant, and in part by the National Science Foundation under Grants No. IIS-0097476 and IIS-9906858.
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dc.identifier.citationMichalski, R. S., Kaufman, K., Pietrzykowski, J., Sniezynski, B. and Wojtusiak, J., "Learning Symbolic User Models for Intrusion Detection: A Method and Initial Results," Proceedings of the Intelligent Information Processing and Web Mining Conference, IIPWM 06, Ustron, Poland, June 19-22, 2006.
dc.identifier.urihttps://hdl.handle.net/1920/1499
dc.language.isoen_US
dc.relation.ispartofseriesP 06-5
dc.titleLearning Symbolic User Models for Intrusion Detection: A Method and Initial Results
dc.typePresentation

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Machine Learning and Inference Laboratory, College of Public Health