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A comparative study of classification models for detection in ip networks intrusions
Published in Asian Research Publishing Network (ARPN)
Volume: 64
Issue: 1
Pages: 107 - 114
Intrusion detection is an essential mechanism to protect computer systems from many attacks. We presented a contribution to the network intrusion detection process using six most representative classification techniques: decision trees, BayesNet, NaïveBayes, Rules, SVM, and Perceptron multi-layer network. In this paper, we presented a feature selection using random forest technique, towards two dimensional dataset reductions that are efficient for the initial and on-going training. The well known KDD'99 Intrusion Detection Dataset is tremendously huge and has been reported by many researchers to have unjustified redundancy, this makes adaptive learning process very time consuming and possibly infeasible. 20 attributes are selected based on errors and time metrics. Performance and accuracy of the six techniques are presented and compared in this paper. Finally, improvement of supervised learning techniques is discussed for detecting new attacks. The different results and experiments performed using the principal component analysis and the enhanced supervised learning technique are thoroughly presented and discussed. We showed that J48 is the best classifier model for IDS with reduced number of features. Finally, avenues for future research are presented. © 2005-2014 JATIT & LLS. All rights reserved.
About the journal
JournalJournal of Theoretical and Applied Information Technology
PublisherAsian Research Publishing Network (ARPN)
Open AccessNo
Concepts (10)
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    And perceptron multi-layer network
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    Decision trees
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    Feature selection
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