AnalyzingNB, DT and NBTree Intrusion Detection Algorithms

Volume 16 , Issue 1 , February 2014 , Pages 69-76

Authors

¹ College of Science, University of Sulaimani

² College of Engineering, University of Sulaimani

This work implements data mining techniques for analysing the performance of Naive Bayes,

C4.5 Decision Tree, and the hybrid of these two algorithms the Naive Bayes Tree (NBTree). The

goal is to select the most efficient algorithm to build a network intrusion detection system (NIDS).

For our experimental analysis we used the new NSL-KDD dataset, which is a modified dataset of

the KDDCup 1999 intrusion detection benchmark dataset, with a split of 66.0% for the training set

and the remainder for the testing set. In the testing process Weka has been used, which is a Java

based open source framework consisting of a collection of machine learning algorithms for data

mining applications. In terms of accuracy the experimental results show that the hybrid NBTree is

more precise than the other two approaches and the decision tree is better than the Naive Bayes

algorithm. Otherwise, in terms of speed of response the Naive Bayes outperform the other two

algorithms followed by Decision Tree and NBTree, respectively.

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