| 基于数据挖掘算法的入侵检测方法 |
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| 引用本文： | 陈小辉. 基于数据挖掘算法的入侵检测方法[J]. 计算机工程, 2010, 36(17): 72-73,76 |
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| 作者姓名： | 陈小辉 |
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| 作者单位： | 淮阴师范学院计算机科学与技术学院,淮安,223300 |
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| 摘 要： | K-Means 和 DBSCAN算法初始聚类中心的选择对数据挖掘结果的影响较大。针对上述问题，利用信息熵改进初始聚类中心选择方法，提高数据挖掘效率。将改进的K-Means算法与DBSCAN算法结合应用于入侵检测系统，对一个通用检测记录集进行异常检测测试，实验结果证明了该方法的有效性。
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| 关 键 词： | 入侵检测系统 数据挖掘 异常记录 聚类算法 |
Intrusion Detection Method Based on Data Mining Algorithm |
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| CHEN Xiao-hui. Intrusion Detection Method Based on Data Mining Algorithm[J]. Computer Engineering, 2010, 36(17): 72-73,76 |
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| Authors: | CHEN Xiao-hui |
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| Affiliation: | (School of Computer Science and Technology, Huaiyin Normal University, Huaian 223300) |
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| Abstract: | How to select original clustering cores of K-Means and DBSCAN is important to the result of data mining. Aiming at the problem, this paper improves the method of selecting original clustering cores via entropy. It applies improved K-Means and DBSCAN to the intrusion detection system, and does anomaly detection test on a common set of records in the system. Experimental result proves that the method is effective. |
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| Keywords: | intrusion detection system data mining anomaly record clustering algorithm |
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