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In the most intrusion detection systems (IDS), a system tries to learncharacteristics of different type of attacks by analyzing packets that sent orreceived in network. These packets have a lot of features. But not all of themis required to be analyzed to detect that specific type of attack. Detectionspeed and computational cost is another vital matter here, because in thesetypes of problems, datasets are very huge regularly. In this paper we tried topropose a very simple and fast feature selection method to eliminate featureswith no helpful information on them. Result faster learning in process ofredundant feature omission. We compared our proposed method with three mostsuccessful similarity based feature selection algorithm including CorrelationCoefficient, Least Square Regression Error and Maximal Information CompressionIndex. After that we used recommended features by each of these algorithms intwo popular classifiers including: Bayes and KNN classifier to measure thequality of the recommendations. Experimental result shows that although theproposed method can't outperform evaluated algorithms with high differences inaccuracy, but in computational cost it has huge superiority over them.

Fast Feature Reduction in intrusion detection datasets