Performance Analysis of Feature Subset Selection Techniques for Intrusion Detection

An intrusion detection system is one of the main defense lines used to provide security to data, information, and computer networks. The problems of this security system are the increased processing time, high false alarm rate, and low detection rate that occur due to the large amount of data containing various irrelevant and redundant features. Therefore, feature selection can solve this problem by reducing the number of features. Choosing appropriate feature selection methods that can reduce the number of features without a negative effect on the classification accuracy is a major challenge. This challenge motivated us to investigate the application of different wrapper feature selection techniques in intrusion detection. The performance of the selected techniques, such as the genetic algorithm (GA), sequential forward selection (SFS), and sequential backward selection (SBS), were analyzed, addressed, and compared to the existing techniques. The efficiency of the three feature selection techniques with two classification methods, including support vector machine (SVM) and multi perceptron (MLP), was compared. The CICIDS2017, CSE-CIC-IDS218, and NSL-KDD datasets were considered for the experiments. The efficiency of the proposed models was proved in the experimental results, which indicated that it had highest accuracy in the selected datasets.