Novel attribute selection technique for an efficient intrusion detection system

Abstract

An intrusion detection system (IDS) is one of the popular countermeasures against network attacks or anomalies in the network. IDS requires data dimension reduction methods such as data filtering, attribute selection, and data clustering to improve the system's performance. Identifying the most significant attributes will produce the computational time complexity drastically. This paper proposes novel attribute selection methods for data reduction. The NSL-KDD dataset is selected for experimental evaluation. The attribute value ratios (AVRs) is calculated using average values with numeric attributes and frequency with binary attributes to select attributes. The proposed attributes selection model is also compared with the traditional attribute section method that is correlation-based feature selection (CFS), information gain (IG) and gain ratio (GR). Further, the accuracy (ACC), detection rate (DR) and false alarm rate (FAR) are computed using the proposed attributes selection model with J48Consolidated classifier and achieve better results than other state-of-the-art methodologies.