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With the tremendous growth of network-based services and sensitive information on networks, network security is getting more and more importance than ever. Intrusion poses a serious security risk in a network environment. The ever growing new intrusion types possesses a serious problem for their detection. The human labelling of the available network audit data instances is usually tedious, time consuming and expensive. In this paper, it applies one of the efficient data mining algorithms called na?ve bayes for anomaly based network intrusion detection. Experimental results on the KDD cup'99 data set show the novelty of approach in detecting network intrusion.
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