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

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dc.contributor.authorKaufman, Kenneth A.
dc.contributor.authorCervone, Guido
dc.contributor.authorMichalski, Ryszard S.
dc.date.accessioned2006-11-03T18:17:23Z
dc.date.available2006-11-03T18:17:23Z
dc.date.issued2003-06
dc.description.abstractThis 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.sponsorshipThe 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.
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dc.format.extent104352 bytes
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dc.format.mimetypeapplication/pdf
dc.identifier.citationKaufman, 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.urihttps://hdl.handle.net/1920/1483
dc.language.isoen_US
dc.relation.ispartofseriesP 03-03
dc.relation.ispartofseriesMLI 03-2
dc.subjectIntrusion detection
dc.subjectSymbolic learning
dc.subjectAttributional calculus
dc.subjectEpisode classification
dc.subjectMultistate conjunctive patterns
dc.titleAn Application of Symbolic Learning to Intrusion Detection: Preliminary Results from the LUS Methodology
dc.typeArticle

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