Association rule based classifier with novel interest measure for intrusion detection

dc.contributor.guideMohan Raj V
dc.coverage.spatialAssociation rule based classifier with novel interest measure for intrusion detection
dc.creator.researcherSivanantham S
dc.date.accessioned2023-10-22T05:56:07Z
dc.date.available2023-10-22T05:56:07Z
dc.date.awarded2023
dc.date.completed2023
dc.date.registered
dc.description.abstractnewlineIn the third research contribution, a novel interest measure named Rule Precision index (RPI) is introduced which helps us to prune Association rules efficiently and the impact is observed in the classification of attack and non-attack data. Here association rule classification is done by using the relevance vector machine. The performance of the proposed classifier is compared with conventional classifiers against three intrusion detection datasets KDD Cup 99, NSL KDD and CICIDS 2017 Dataset. The overall implementation of the research work is done in the WEKA tool from which it is proved that the proposed methodology can attain an accurate classification outcome which is better than the existing research methodologies.
dc.description.note
dc.format.accompanyingmaterialNone
dc.format.dimensions21 cm.
dc.format.extentxv,145 P.
dc.identifier.urihttp://hdl.handle.net/10603/519774
dc.languageEnglish
dc.publisher.institutionFaculty of Information and Communication Engineering
dc.publisher.placeChennai
dc.publisher.universityAnna University
dc.relationp.136-144
dc.rightsuniversity
dc.source.universityUniversity
dc.subject.keywordComputer Science
dc.subject.keywordComputer Science Information Systems
dc.subject.keywordEngineering and Technology
dc.subject.keywordIntrusion Detection
dc.subject.keywordKDD
dc.subject.keywordWEKA
dc.titleAssociation rule based classifier with novel interest measure for intrusion detection
dc.title.alternative
dc.type.degreePh.D.

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