AnalyzingNB, DT and NBTree Intrusion Detection Algorithms


Deeman Yousif Mahmood1, Dr. Mohammed Abdullah Hussein2

College of Science, University of Sulaimani, 2 College of Engineering, University of Sulaimani




Abstract:
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.

Keywords: Decision Tree (C4.5); Intrusion detection System (IDS); Naïve Bayes (NB); NBTree; NSL-
KDD; Weka



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