Fuzzy clustering method based on genetic algorithm in intrusion detection study

被引：0
|
作者：
Huang, Min-Ming [1 ]
Lin, Bo-Gang [1 ]
机构：
[1] College of Mathematics and Computer Science, Fuzhou University, Fuzhou 350108, China
来源：
Tongxin Xuebao/Journal on Communications | 2009年 / 30卷 / 11 A期
关键词：
Clustering algorithms - Copying - Genetic algorithms - Fuzzy clustering - Intrusion detection;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
Regarding the problem that fuzzy c-means algorithm (FCM) was sensitive to the initial value and converging to the local infinitesimal point easily, applies genetic algorithm to optimization of the FCM algorithm. Firstly, the results of FCM will be sent to the genetic algorithm for optimization, then the new results again used in FCM to obtain the most advantage of the overall situation. The experimental result shows that the algorithm can effectively detect anomaly intrusions behavior of special target and be better than FCM algorithm, and have a strong global optimization and faster convergence speed.
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页码：140 / 145
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