| 一种K-MEANS算法在网络异常检测中的应用 |
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| 引用本文： | 刘涛,马晓宇,胡景.一种K-MEANS算法在网络异常检测中的应用[J].微电子学与计算机,2012,29(5):42-45. |
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| 作者姓名： | 刘涛 马晓宇 胡景 |
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| 作者单位： | 1. 西安科技大学通信学院,陕西西安,710054
2. 陕西高速交通工贸有限公司,陕西西安,710054 |
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| 基金项目： | 陕西省教育厅科学研究计划 |
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| 摘 要： | 在研究K-MEANS算法和网络入侵的基础上将一种已知聚类中心的K-MEANS聚类算法用于网络的异常检测中.该算法避免了由于传统聚类算法随机选取初始聚类中心而带来的网络异常检测中检测率低的问题.在实例中验证了该算法的可行性和优越性.结果表明该算法相对传统聚类算法在检测率方面有了很大提高,并且能通过无监督学习的方法来获得对新型攻击的检测.
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| 关 键 词： | 入侵检测 异常检测 K-MEANS |
A K-MEANS Algorithm Applied in Network Anomaly Detection |
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| LIU Tao,MA Xiao-yu,HU Jing.A K-MEANS Algorithm Applied in Network Anomaly Detection[J].Microelectronics & Computer,2012,29(5):42-45. |
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| Authors: | LIU Tao MA Xiao-yu HU Jing |
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| Affiliation: | 1(1 School of Communication Engineering,Xi′an University of Science and Technology,Xi′an 710054,China; 2 Shaanxi Rapid Transit Industry and Trade Co.,Ltd.,Xi′an 710054,China) |
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| Abstract: | This paper studied a known cluster centers of k-means clustering algorithm for anomaly detection of network.The algorithm avoids the low detection rate of network anomaly detection problems for traditional clustering algorithms for random selecting initial cluster centers.Experiment demonstrated that the algorithm is feasible.Results showed that this algorithm comparing to traditional clustering algorithms in terms of detection rates have been greatly improved.Through the unsupervised learning method to obtain the detection of new attacks. |
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| Keywords: | intrusion detection anomaly detection K-MEANS |
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