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

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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.

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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.