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Application of a New Fuzzy Clustering Algorithm in Intrusion Detection

Application of a New Fuzzy Clustering Algorithm in Intrusion Detection
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摘要 This paper presents a new Section Set Adaptive FCM algorithm.The algorithm solved the shortcomings of local optimality,unsure classification and clustering numbers ascertained previously.And it improved on the architecture of FCM al- gorithm,enhanced the analysis for effective clustering.During the clustering processing,it may adjust clustering numbers dy- namically.Finally,it used the method of section set decreasing the time of classification.By experiments,the algorithm can im- prove dependability of clustering and correctness of classification. This paper presents a new Section Set Adaptive FCM algorithm. The algorithm solved the shortcomings of local optimality, unsure classification and clustering numbers ascertained previously. And it improved on the architecture of FCM algorithm,enhanced the analysis for effective clustering. During the clustering processing,it may adjust clustering numbers dy- namically. Finally,it used the method of section set decreasing the time of classification. By experiments,the algorithm can im- prove dependability of clustering and correctness of classification.
作者 WU Tiefeng
机构地区 Jiamusi University Xidian University
出处 《现代电子技术》 2008年第4期100-102,共3页 Modern Electronics Technique
基金 Science and Researching Foundation of Jiamusi University(L2006-12)
关键词 模糊聚类算法 干扰检测 计算机技术 FCM Fuzzy clustering Clustering Numbers Section Set Adaptive Algorithm Network Security
分类号 TP391 [自动化与计算机技术—计算机应用技术]
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参考文献7

  • 1Dai Yingxia, Lian Yifeng, Yu Hang. System Safety and Intrusion Detection[M]. Tsinghua University Press, 2002.
  • 2Hiren Shah,Jeffrey Undercoffer, Anupam Joshi. Fuzzy Cluster for Intrusion Detection[C]. IEEE, International Conference on Fuzzy System. 2003 : 1 274 - 1 278.
  • 3James C Bezdek. A Convergence Theorem for the Fuzzy ISODATA Clustering Algorithms. IEEE Transaction on Pattern Analysis and Machine Intelligence, 1980, ( 1 ) :PAMI -2.
  • 4Gao XinBo. Fuzzy Cluster Analysis and Applications. Xidian University Press, 2004.
  • 5Xie X L, Beni G. A Validity Measure for Fuzzy Clustering [J]. IEEE Trans. Patt. Anal. Machine. Intell. , 1991,13(8) 841 - 847.
  • 6Kamel S Mohamed. New Algorithms for Solving the Fuzzy C means Clustering Problem[J]. Pattern Recognition, 1994,27:421 -428.
  • 7饶鲜,董春曦,杨绍全.基于支持向量机的入侵检测系统[J].软件学报,2003,14(4):798-803. 被引量：135

二级参考文献6

  • 1[1]Forrest S, Perrelason AS, Allen L, Cherukur R. Self_Nonself discrimination in a computer. In: Rushby J, Meadows C, eds. Proceedings of the 1994 IEEE Symposium on Research in Security and Privacy. Oakland, CA: IEEE Computer Society Press, 1994. 202～212.
  • 2[2]Ghosh AK, Michael C, Schatz M. A real-time intrusion detection system based on learning program behavior. In: Debar H, Wu SF, eds. Recent Advances in Intrusion Detection (RAID 2000). Toulouse: Spinger-Verlag, 2000. 93～109.
  • 3[3]Lee W, Stolfo SJ. A data mining framework for building intrusion detection model. In: Gong L, Reiter MK, eds. Proceedings of the 1999 IEEE Symposium on Security and Privacy. Oakland, CA: IEEE Computer Society Press, 1999. 120～132.
  • 4[4]Vapnik VN. The Nature of Statistical Learning Theory. New York: Spring-Verlag, 1995.
  • 5[5]Lee W, Dong X. Information-Theoretic measures for anomaly detection. In: Needham R, Abadi M, eds. Proceedings of the 2001 IEEE Symposium on Security and Privacy. Oakland, CA: IEEE Computer Society Press, 2001. 130～143.
  • 6[6]Warrender C, Forresr S, Pearlmutter B. Detecting intrusions using system calls: Alternative data models. In: Gong L, Reiter MK, eds. Proceedings of the 1999 IEEE Symposium on Security and Privacy. Oakland, CA: IEEE Computer Society Press, 1999. 133～145.

共引文献134

现代电子技术
2008年 第4期

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