| 基于核模糊C均值的异常检测方法 |
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| 引用本文： | 王选宏,肖云.基于核模糊C均值的异常检测方法[J].科学技术与工程,2010,10(23). |
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| 作者姓名： | 王选宏 肖云 |
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| 作者单位： | 1. 西安邮电学院通信工程系,西安,710121
2. 西北大学信息科学与技术学院,西安,710127 |
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| 基金项目： | 陕西省教育厅自然科学专项，西安邮电学院中青年科研基金项目 |
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| 摘 要： | 探索聚类方法在异常检测中的应用,提出了一种基于核的模糊C均值的异常检测方法.该方法使用核的模糊C均值对网络数据进行聚类,并使用基于簇内距离的判断规则对聚类结果进行标定,从而识别出攻击.使用KDD CUP1999数据集进行实验,结果表明本文表现出了高检测率和低误报率的良好性能.
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| 关 键 词： | 异常检测 聚类 核模糊C均值 簇内距离 |
| 收稿时间： | 5/23/2010 7:26:27 PM |
| 修稿时间： | 2010/5/29 0:00:00 |
Anomaly detection method based on Kernel Fuzzy C-Means Clustering Algorithm |
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| wangxuanhong and xiaoyun.Anomaly detection method based on Kernel Fuzzy C-Means Clustering Algorithm[J].Science Technology and Engineering,2010,10(23). |
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| Authors: | wangxuanhong and xiaoyun |
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| Institution: | Dept. of Information Science & Technology, Northwest University |
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| Abstract: | A clustering algorithm is used in anomaly detection, and an anomaly detection method based on kernel fuzzy C-means clustering algorithm is proposed. This method uses kernel fuzzy C-means clustering algorithm to get clustering results, then a judging rule based on intra-cluster distance is used to detect attacks. The data set of KDD CUP 99 is used to test, and the results show that the proposed method has good performance with higher detection rate and lower false rate. |
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| Keywords: | anomaly detection clustering kernel fuzzy C-means intra-cluster distance |
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