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Intrusion detection systems (IDS) are widely studied by researchers nowadaysdue to the dramatic growth in network-based technologies. Policy violations andunauthorized access is in turn increasing which makes intrusion detectionsystems of great importance. Existing approaches to improve intrusion detectionsystems focus on feature selection or reduction since some features areirrelevant or redundant which when removed improve the accuracy as well as thelearning time. In this paper we propose a hybrid feature selection method usingCorrelation-based Feature Selection and Information Gain. In our work we applyadaptive boosting using na\"ive Bayes as the weak (base) classifier. The keypoint in our research is that we are able to improve the detection accuracywith a reduced number of features while precisely determining the attack.Experimental results showed that our proposed method achieved high accuracycompared to methods using only 5-class problem. Correlation is done usingGreedy search strategy and na\"ive Bayes as the classifier on the reducedNSL-KDD dataset.

Improving the Performance of Multi-class Intrusion Detection Systems using Feature Reduction