  Feature selection is an indispensable preprocessing step when mining huge
datasets that can significantly improve the overall system performance.
Therefore in this paper we focus on a hybrid approach of feature selection.
This method falls into two phases. The filter phase select the features with
highest information gain and guides the initialization of search process for
wrapper phase whose output the final feature subset. The final feature subsets
are passed through the Knearest neighbor classifier for classification of
attacks. The effectiveness of this algorithm is demonstrated on DARPA KDDCUP99
cyber attack dataset.
