Please use this identifier to cite or link to this item: http://hdl.handle.net/10603/519774
Title: Association rule based classifier with novel interest measure for intrusion detection
Researcher: Sivanantham S
Guide(s): Mohan Raj V
Keywords: Computer Science
Computer Science Information Systems
Engineering and Technology
Intrusion Detection
KDD
WEKA
University: Anna University
Completed Date: 2023
Abstract: newlineIn 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.
Pagination: xv,145 P.
URI: http://hdl.handle.net/10603/519774
Appears in Departments:Faculty of Information and Communication Engineering

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01_title.pdfAttached File595.56 kBAdobe PDFView/Open
02_prelim_pages.pdf3.3 MBAdobe PDFView/Open
03_content.pdf735.89 kBAdobe PDFView/Open
04_abstract.pdf690.33 kBAdobe PDFView/Open
05_chapter 1.pdf1.03 MBAdobe PDFView/Open
06_chapter 2.pdf758.56 kBAdobe PDFView/Open
07_chapter 3.pdf1.12 MBAdobe PDFView/Open
08_chapter 4.pdf1.09 MBAdobe PDFView/Open
09_chapter 5.pdf1.17 MBAdobe PDFView/Open
10_annexures.pdf106.21 kBAdobe PDFView/Open
80_recommendation.pdf59.97 kBAdobe PDFView/Open
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