| 聚类分析在入侵检测系统中的改进 |
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| 引用本文： | 邹伟平,邓庚盛.聚类分析在入侵检测系统中的改进[J].科技广场,2011(7). |
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| 作者姓名： | 邹伟平 邓庚盛 |
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| 作者单位： | 1. 南昌大学信息工程学院,江西南昌,330031
2. 南昌大学网络中心,江西南昌,330031 |
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| 摘 要： | 本文针对传统的聚类算法在入侵检测系统中的不足,提出一种基于密度的初始聚类中心的选择方法,可克服普通K-Means中的需人工确定K值的问题,用此算法改进的入侵检测模型能够获得很好的聚类效果。对比实验结果,发现使用改进后的算法与传统的K-Means相比可以获得更高的检测率和较低的误报率。
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| 关 键 词： | 数据挖掘 入侵检测 K-Means算法 聚类分析 |
A Improved Intrusion Detection about Clustering Analysis |
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| Zou Weiping Deng Gengsheng.A Improved Intrusion Detection about Clustering Analysis[J].Science Mosaic,2011(7). |
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| Authors: | Zou Weiping Deng Gengsheng |
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| Institution: | Zou Weiping Deng Gengsheng(1.School of Information and Engineering,Nanchang University,Jiangxi Nanchang 330031,2.Network Centre,Jiangxi Nanchang 330031) |
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| Abstract: | This essay focus on the shortcoming of clustering algorithm on the intrusion detection system,put forward a original clustering center selection based on destiny which can solve the problem of K-Means algorithm need manual set K value.On the basic of this algorithm,The intrusion detection module can make a good effect.After the compared experiments show that the advanced clustering algorithm can improve the efficiency of data clustering. |
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| Keywords: | Data Mining Intrusion Detection K-Means Algorithm Clustering Analysis |
| 本文献已被 CNKI 万方数据 等数据库收录！ |
