Rough Set Theory Based Hybrid Method for Network Intrusion Detection

Abstract

In this paper, we propose an intrusion detection method that combines rough set theory and Fuzzy C-Means for network intrusion detection. The first step consists of feature selection which is based on rough set theory. The next phase is clustering by using Fuzzy C-Means. Rough set theory is an efficient tool for further reducing redundancy. Fuzzy C-Means allows objects which are belong to several clusters simultaneously, with different degrees of membership. To evaluate the performance of the introduced approach, we applied them to the international Knowledge Discovery and Data mining intrusion detection dataset. In the experimentations, we compare the performance of the rough set theory based hybrid method for network intrusion detection. Experimental results illustrate that our algorithm is accurate model for handling complex attack patterns in large network. And the method can increase the efficiency and reduce the dataset by looking for overlapping categories.