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Supervised by Ministry of Industry and Information Technology of The People's Republic of China Sponsored by Harbin Institute of Technology Editor-in-chief Yu Zhou ISSNISSN 1005-9113 CNCN 23-1378/T

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Related citation:Na Jiao.Two Hybrid Methods Based on Rough Set Theory for Network Intrusion Detection[J].Journal of Harbin Institute Of Technology(New Series),2014,21(6):22-27.DOI:10.11916/j.issn.1005-9113.2014.06.005.
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Two Hybrid Methods Based on Rough Set Theory for Network Intrusion Detection
Author NameAffiliation
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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