Volume 7 Number 12 (Dec. 2012)
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JSW 2012 Vol.7(12): 2823-2831 ISSN: 1796-217X
doi: 10.4304//jsw.7.12.2823-2831

Research on Intrusion Detection Model of Heterogeneous Attributes Clustering

Linquan Xie1, Ying Wang1, Fei Yu2, Chen Xu2, 3 and GuangXue Yue4
1School of Science, Jiangxi University of Science and Technology, 341000 Ganzhou, China
2Jiangsu Provincial Key Laboratory for Computer Information Processing Technology, Soochow University, 215000 Soochow, China
3School of Information Science and Engineering, Hunan University, 416000 Changsha, China
4Department of Computer Science and Technology, Huaihua University, Huaihua, China

Abstract—A fuzzy clustering algorithm for intrusion detection based on heterogeneous attributes is proposed in this paper. Firstly, the algorithm modifies the comparability measurement for the categorical attributes according to the formula of Hemingway; then, for the shortages of fuzzy Cmeans clustering algorithm: initialize sensitively and easy to get into the local optimum, the presented new algorithm is optimized by GuoTao approach. We simulate our algorithm with the KDDCUP99 data set, and the results show that the convergence rate of the new algorithm is faster than the original fuzzy C-means clustering algorithm and the performance of our algorithm is more stable.

Index Terms—Intrusion Detection, Heterogeneous Attributes, Fuzzy Clusterin.


Cite: Linquan Xie, Ying Wang, Fei Yu, Chen Xu< and GuangXue Yue, "Research on Intrusion Detection Model of Heterogeneous Attributes Clustering," Journal of Software vol. 7, no. 12, pp. 2823-2831, 2012.

General Information

ISSN: 1796-217X (Online)
Frequency:  Bimonthly (Since 2020)
Editor-in-Chief: Prof. Antanas Verikas
Executive Editor: Ms. Yoyo Y. Zhou
Abstracting/ Indexing: DBLP, EBSCO, Google Scholar, ProQuest, INSPEC(IET), ULRICH's Periodicals Directory, WorldCat, etc
E-mail: jsw@iap.org
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