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Two Hybrid Methods Based on Rough Set Theory for Network Intrusion Detection

本站小编 哈尔滨工业大学/2019-10-24

Two Hybrid Methods Based on Rough Set Theory for Network Intrusion Detection

Na Jiao

(Dept. of Information Science and Technology, East China University of Political Science and Law, Shanghai 201620, China)



Abstract:

In this paper, we propose two intrusion detection methods which combine 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 the objects to belong to several clusters simultaneously, with different degrees of membership. To evaluate the performance of the introduced approaches, we apply them to the international Knowledge Discovery and Data mining intrusion detection dataset. In the experimentations, we compare the performance of two rough set theory based hybrid methods for network intrusion detection. Experimental results illustrate that our algorithms are accurate models for handling complex attack patterns in large network. And these two methods can increase the efficiency and reduce the dataset by looking for overlapping categories.

Key words:  rough set theory  Fuzzy C-Means  network security  intrusion detection

DOI:10.11916/j.issn.1005-9113.2014.06.005

Clc Number:TP393.08

Fund:


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