Adaptive Intrusion Detection Using Machine Learning

In this paper, the authors apply PCA for feature selection with Na?ve Bayes for classification in order to build a network Intrusion Detection System. For experimental analysis, KDDCup 1999 intrusion detection benchmark dataset have been used. The 2 class classification is performed. The experimental results show that the proposed approach is very accurate with low false positive rate and takes less time in comparison to other existing approaches while building an efficient network Intrusion Detection System.

Provided by: International Journal of Computer Science and Network Security Topic: Security Date Added: Mar 2013 Format: PDF

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