Forming an optimal feature set for classifying network intrusions involving multiple feature selection methods

High computational cost has always been a constraint in processing huge network intrusion data. This problem can be mitigated through feature selection to reduce the size of the network data involved. In this research work, we first consider existing feature selection methods that are computationally feasible for processing huge network intrusion datasets. Each of the feature selection methods was treated as an expert capable of identifying useful features from the datasets. A feature that is selected by these experts implies its importance in detecting network intrusions. The important features were subsequently grouped to form feature sets based on the frequency of selection. One such feature set was able to produce classification results comparable to feature sets generated by single feature selection methods and was also comparable to classification results of the winner of KDD CUP competition.