An Efficient Network Intrusion Detection using Information Gain and Hierarchical Clustering

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S. Sethuramalingam
Dr.E.R. Naganathan


With high dimensionality data the classification may lead to wrong results and also needs more resources especially in terms of time
by considering all the features of the data set. The redundancy and inconsistency present in the data set result in the misclassification and also
increase time and other resource utilization. In order to improve the efficiency of intrusion detection eliminates those features which are
redundant and inconsistent. In this paper, we have proposed a new algorithm to identify the significance of features based on the Information
Gain. Clustering and Hierarchical Clustering are carried out on features which are more relevant. Hierarchical clustering has yielded better
performance. The experiment is conducted with NLS-KDD network intrusion data set. It classifies the data set with good accuracy.


Keywords: Intrusion, Anomaly, Misuse, Hierarchical Clustering, Information Gain


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