Improved feature selection method for features reduction in intrusion detection systems

Abstract

Many methods have been used to build intrusion detection system based on the intended aim to be achieved in with the selected method. The hybrid methods (more than one method) usually gives better results and accuracy. The recent developments and popularization of network & information technologies have necessitated the need for network information security. Human-based smart intrusion detection systems (IDSs) are built with the capability to either warn or intercept network intrusion; this is not possible with the conventional network security systems. However, most information security studies have focused on improvement of the effectiveness of smart network IDSs. This study used TLBO algorithm as a feature selection algorithm to choose the best subset features and SVM classifier to classify the packet if it is intrusion or normal packet, two machine learning datasets used to test the proposed algorithm, the results show that the proposed algorithm perform better than many of the existing work in IDS.