Network Intrusion Detection Through Genetic Feature Selection

This paper presents the novel feature selection method that maximizes class separability between normal and attack patterns of computer network connections. Recent years have witnessed increased interest in using a genetic algorithm to improve the performance of a classifier. In this paper we focus on selecting a robust feature subset based on the genetic optimization procedure in order to improve a true positive intrusion detection rate. During the evaluation phase, the performance of proposed approach is contrasted against one of state-of-the-art feature selection method using a naive Bayesian classifier. Experimental results show that the proposed approach is especially effective in terms of detecting totally unknown attack patterns