| 基于模糊C均值算法的入侵检测方法 |
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| 引用本文： | 林荣亮,张文波.基于模糊C均值算法的入侵检测方法[J].计算机与数字工程,2012,40(5):82-83,148. |
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| 作者姓名： | 林荣亮 张文波 |
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| 作者单位： | 中国人民解放军92854部队 湛江524005 |
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| 摘 要： | 聚类分析是一种有效的异常入侵检测方法，可用以在网络数据集中区分正常流量和异常流量。采用模糊C均值聚类算法对网络流量样本集进行划分，从中区分正常流量和异常流量，并针对入侵检测问题的特性提出了聚类中心确定方法。最后，利用KDD99数据集进行实验，证明该算法能够有效地发现异常流量。
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| 关 键 词： | 模糊聚类 入侵检测 距离测度 模糊C均值 |
An Approach for Intrusion Detection Based on Fuzzy C-means Algorithm |
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| LIN Rongliang
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ZHANG Wenbo.An Approach for Intrusion Detection Based on Fuzzy C-means Algorithm[J].Computer and Digital Engineering,2012,40(5):82-83,148. |
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| Authors: | LIN Rongliang ZHANG Wenbo |
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| Affiliation: | (No.92854 Troops of PLA,Zhanjiang 524005) |
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| Abstract: | Clustering analysis is an effective method of anomaly intrusion detection,which can find normal flow and abnormal flow in the network data set.Fuzzy C-means clustering algorithm is applied to classify the network traffic data into normal flow and abnormal flow.A new clustering center method which is designed for intrusion detecting problem specially is provided in this paper.Finally,KDD 99 data set is used for the illustrative example,and the result proves that this algorithm could discover the abnormal flows effectively. |
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| Keywords: | fuzzy clustering intrusion detection distance measurement fuzzy C-means algorithm |
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