  In the most intrusion detection systems (IDS), a system tries to learn
characteristics of different type of attacks by analyzing packets that sent or
received in network. These packets have a lot of features. But not all of them
is required to be analyzed to detect that specific type of attack. Detection
speed and computational cost is another vital matter here, because in these
types of problems, datasets are very huge regularly. In this paper we tried to
propose a very simple and fast feature selection method to eliminate features
with no helpful information on them. Result faster learning in process of
redundant feature omission. We compared our proposed method with three most
successful similarity based feature selection algorithm including Correlation
Coefficient, Least Square Regression Error and Maximal Information Compression
Index. After that we used recommended features by each of these algorithms in
two popular classifiers including: Bayes and KNN classifier to measure the
quality of the recommendations. Experimental result shows that although the
proposed method can't outperform evaluated algorithms with high differences in
accuracy, but in computational cost it has huge superiority over them.
