Abstract
In this paper, a new learning algorithm for adaptive network intrusion detection using principal component analysis, decision tree and Naive Bayesian classifier is presented. First we use PCA (Principal Component Analysis) to remove unimportant information like the noise in the data sets, to reduce the dimension, and to retain the important information as much as possible. Then we use the Decision tree and Naive Bayesian algorithm to make Intrusion Detection Model. We have tested the performance of our proposed algorithm on the KDD99 benchmark intrusion detection dataset. The experimental result prove that the proposed algorithm achieved high detection rates (DR), low false positive (FP) and low false negative (FN) for different types of network intrusions.
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