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Using an Improved Clustering Method to Detect Anomaly Activities 被引量：3

Using an Improved Clustering Method to Detect Anomaly Activities
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摘要 In this paper, an improved k-means based clustering method （IKCM） is proposed. By refining the initial cluster centers and adjusting the number of clusters by splitting and merging procedures, it can avoid the algorithm resulting in the situation of locally optimal solution and reduce the number of clusters dependency. The IKCM has been implemented and tested. We perform experiments on KDD-99 data set. The comparison experiments with H-means＋also have been conducted. The results obtained in this study are very encouraging. In this paper, an improved k-means based clustering method （IKCM） is proposed. By refining the initial cluster centers and adjusting the number of clusters by splitting and merging procedures, it can avoid the algorithm resulting in the situation of locally optimal solution and reduce the number of clusters dependency. The IKCM has been implemented and tested. We perform experiments on KDD-99 data set. The comparison experiments with H-means＋also have been conducted. The results obtained in this study are very encouraging.
作者 LI Han ZHANG Nan BAO Lihui
机构地区 Division of Basic Courses School of Electronic and Information Engineering
出处 《Wuhan University Journal of Natural Sciences》 CAS 2006年第6期1814-1818,共5页 武汉大学学报（自然科学英文版）
基金 Supported by the Beijing Municipal Commission ofEducation Science and Technology Project (KM200511232004)
关键词 clustering analysis anomaly detection intrusion detection K-MEANS clustering analysis anomaly detection intrusion detection k-means
分类号 TP393 [自动化与计算机技术—计算机应用技术]
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参考文献10

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同被引文献20

  • 1卿斯汉,蒋建春,马恒太,文伟平,刘雪飞.入侵检测技术研究综述[J].通信学报,2004,25(7):19-29. 被引量：235
  • 2蒋盛益,李庆华.一种增强的k-means聚类算法[J].计算机工程与科学,2006,28(11):56-59. 被引量：15
  • 3Lee W K,Stolfo S J,Mok K W. A data mining framework for building intrusion detection models[ C]//Proceeding of the IEEE Symposium on Security and Privacy. California, IEEE Computer Society Press, 1999:120- 132.
  • 4Erbaeher R F, Walker K L, Frincke D A. Intrusion and misuse detection in large-scale systems[J]. IEEE Computer Graphics and Applications, 2002,22 (1) : 38 - 47.
  • 5Richard J Hathaway,James C Bezdek. Extending fuzzy and probability clustering to very large data sets[ J ]. Computational Statistics & Data Analysis,2006,51(1):215-234.
  • 6Hansen P, Mladenovic N. J- means: a new local search heuristic for minimum sum - of - squares clustering [ J ]. Pattern Recognition,2002,34(2) :405 - 413.
  • 7University of California. KDD CUP 1999 dataset [ EB/ OL]. (1999 - 10 - 28) [2010 - 1 - 20]. http://kdd, ies. uci. edu/databases/kddcup99/kddcup99, html.
  • 8Lee W K, Stolfo S J, Mok K W. A data mining framework for building intrusion detection models [ C ] //Proceeding of the IEEE Symposium on Security and Privacy. California:IEEE Computer Socidy Press, 1999 : 120 - 132.
  • 9Erbacher R F, Walker K L, Frincke D A. Intrusion and misuse detection in large -scale systems[ J]. IEEE Computer Graphics and Applications,2002, 22(1) :38 -47.
  • 10Richard J Hathaway, James C Bezdek. Extending fuzzy and probability clustering to very large data sets [ J ]. Computational Statistics & Data Analysis,2006,51 ( 1 ) :215 -234.

引证文献3

二级引证文献10

Wuhan University Journal of Natural Sciences
2006年 第6期

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