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

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dc.contributor.author Michalski, Ryszard S. en_US
dc.contributor.author Kaufman, Kenneth A. en_US
dc.contributor.author Pietrzykowski, Jaroslaw en_US
dc.contributor.author Śnieżyński, Bartłomiej en_US
dc.contributor.author Wojtusiak, Janusz en_US
dc.date.accessioned 2006-11-03T18:17:37Z
dc.date.available 2006-11-03T18:17:37Z
dc.date.issued 2006-06 en_US
dc.identifier.citation Michalski, 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. en_US
dc.identifier.uri http://hdl.handle.net/1920/1499
dc.description.abstract This 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. en_US
dc.description.sponsorship This 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. en_US
dc.format.extent 2188 bytes
dc.format.extent 724308 bytes
dc.format.mimetype text/xml
dc.format.mimetype application/pdf
dc.language.iso en_US en_US
dc.relation.ispartofseries P 06-5 en_US
dc.title Learning Symbolic User Models for Intrusion Detection: A Method and Initial Results en_US
dc.type Presentation en_US


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