Reducing Network Intrusion Detection using Association rule and Classification algorithms

International Journal of Computer Trends and Technology (IJCTT)          
© - Issue 2012 by IJCTT Journal
Volume-3 Issue-6                           
Year of Publication : 2012
Authors :K.Keerthi, P.Sreenivas.


K.Keerthi, P.Sreenivas."Reducing Network Intrusion Detection using Association rule and Classification algorithms "International Journal of Computer Trends and Technology (IJCTT),V3(6):577-579 Issue 2012 .ISSN Published by Seventh Sense Research Group.

Abstract: -IDS (Intrusion Detection system) is an active and driving defense technology. This project mainly focuses on intrusion detection based on data mining. Data mining is to identify valid, novel, potentially useful, and ultimately understandable patterns in massive data. This project presents an approach to detect intrusion based on data mining frame work. Intrusion Detection System (IDS) is a popular tool to secure network. Applying data mining has increased the quality of intrusion detection neither as anomaly detection or misused detection from large scale network traffic transaction. Association rules is a popular technique to produce a quality misused detection. However, the weaknesses of association rules is the fact that it often produced with thousands rules which reduce the performance of IDS.


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KeywordsAssociation Rules, Association Rule Mining, Ontology, correlation measures, user constraints.