Abstract
In this paper, we introduce a new intrusion detection method based on rough sets for network. 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 approaches, we applied them to the international Knowledge Discovery and Data mining intrusion detection dataset. Experimental results illustrate that our algorithm is accurate models for handling complex attack patterns in large network.
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