Analysis of Heuristic based Feature Reduction method in Intrusion Detection System

Increasing attacks in the internet domain has led to the need of intrusion detection system (IDS) and many researchers have trying to improve the performance of IDS. Network-based IDS tries to detect intrusion using network-based features. KDD 1999 dataset has been widely used as benchmark intrusion detection dataset. It has 41 features. This work tries to extract a subset of 41 features without degrading the performance of IDS. For dimensionality reduction, this work uses Information gain(IG), Gain ratio(GR) and Correlation-based feature selection algorithms. This work also proposes a heuristic based dimensionality reduction approach to further improve the performance of the aforementioned feature selection algorithms.