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An Application of Symbolic Learning to Intrusion Detection: Preliminary Results from the LUS Methodology

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dc.contributor.author Kaufman, Kenneth A.
dc.contributor.author Cervone, Guido
dc.contributor.author Michalski, Ryszard S.
dc.date.accessioned 2006-11-03T18:17:23Z
dc.date.available 2006-11-03T18:17:23Z
dc.date.issued 2003-06
dc.identifier.citation Kaufman, K., Cervone, G. and Michalski, R. S., "An Application of Symbolic Learning to Intrusion Detection: Preliminary Results From the LUS Methodology," Reports of the Machine Learning and Inference Laboratory, MLI 03-2, George Mason University, Fairfax, VA, June, 2003.
dc.identifier.uri https://hdl.handle.net/1920/1483
dc.description.abstract This paper describes briefly a method for applying AQ symbolic learning to problems of computer user modeling and intrusion detection. The method, called LUS (Learning User Signatures), learns models of users’ interaction in the form of sets of rules in attributional calculus, and signals a possible intrusion when a user interaction with a computer violates the model. An important characteristic of LUS is that the generated user signatures are easy to interpret and understand. We describe briefly the LUS method, the machine learning and inference tools developed to support it, and selected initial experimental results from its application to real-world data.
dc.description.sponsorship The Laboratory's research activities are supported in part by the UMBC/LUCITE #32 grant, and in part by the National Science Foundation under Grants No. IIS-9906858 and IIS-0097476.
dc.format.extent 474 bytes
dc.format.extent 104352 bytes
dc.format.mimetype text/xml
dc.format.mimetype application/pdf
dc.language.iso en_US en_US
dc.relation.ispartofseries P 03-03 en_US
dc.relation.ispartofseries MLI 03-2
dc.subject intrusion detection en_US
dc.subject symbolic learning en_US
dc.subject attributional calculus en_US
dc.subject episode classification en_US
dc.subject multistate conjunctive patterns en_US
dc.title An Application of Symbolic Learning to Intrusion Detection: Preliminary Results from the LUS Methodology en_US
dc.type Article en_US


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