A Feature Selection Approach for Network Intrusion Detection

Processing huge amount of collected network data to identify network intrusions needs high computational cost. Reducing features in the collected data may therefore solve the problem. We proposed an approach for obtaining optimal number of features to build an efficient model for intrusion detection system (IDS). Two feature selection algorithms were involved to generate two feature sets. These two features sets were then utilized to produce a combined and a shared feature set, respectively. The shared feature set consisted of features agreed by the two feature selection algorithms and therefore considered important features for identifying intrusions. Human intervention was then conducted to find an optimal number of features in between the combined (maximum) and shared feature sets (minimum). Empirical results showed that the proposed feature set gave equivalent results compared to the feature sets generated by the selected feature selection methods, and combined feature sets.