Fuzzy clustering method based on genetic algorithm in intrusion detection study

HUANG Min-ming LIN Bo-gang

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Fuzzy clustering method based on genetic algorithm in intrusion detection study

更新时间：2024-10-14

- Fuzzy clustering method based on genetic algorithm in intrusion detection study
- Vol. 30, Issue S2, Pages: 140-145(2009)
- 作者机构：
  
  福州大学数学与计算机科学学院
- 作者简介：
- 基金信息：
- DOI：
  CLC： TP18;TP393.08
- Published：2009
- 稿件说明：
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HUANG Min-ming LIN Bo-gang. Fuzzy clustering method based on genetic algorithm in intrusion detection study[J]. 2009, 30(S2): 140-145.
DOI：

HUANG Min-ming LIN Bo-gang. Fuzzy clustering method based on genetic algorithm in intrusion detection study[J]. 2009, 30(S2): 140-145. DOI：

摘要

针对模糊C均值算法(FCM)对初始值敏感以及容易收敛于局部极小点的缺陷

将遗传算法应用于FCM算法的优化设计中。先将FCM的结果送遗传算法优化

得到的结果再次运用FCM聚类

取得全局最优点。实验结果表明该算法可以有效地检测特定对象异常入侵行为

且检测度优于FCM算法

可以有选择地收敛到全局最优点及较快的收敛速度。

Abstract

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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