An improved network intrusion detection technique based on k-means clustering via Naïve bayes classification

As network attacks have increased in number and severity over the past few years, intrusion detection system (IDS) is increasingly becoming a critical component to secure the network. Due to large volumes of security audit data as well as complex and dynamic properties of intrusion behaviors, optimizing performance of IDS becomes an important open problem that is receiving more and more attention from the research community. Intrusion poses a serious security risk in a network environment. The ever growing new intrusion types pose a serious problem for their detection. The human labeling of the available network audit data instances is usually tedious, time consuming and expensive. In this paper, we apply one of the efficient data mining algorithms called k-means clustering via naïve bayes classification for anomaly based network intrusion detection. Experimental results on the KDD cup'99 data set show the novelty of our approach in detecting network intrusion. It is observed that the proposed technique performs better in terms of Detection rate when applied to KDD'99 data sets compared to a naïve bayes based approach.