| 基于核主成分提取和支持向量机的入侵检测 |
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| 引用本文： | 孙宗宝,孙名松. 基于核主成分提取和支持向量机的入侵检测[J]. 信息技术, 2007, 31(7): 29-31 |
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| 作者姓名： | 孙宗宝 孙名松 |
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| 作者单位： | 哈尔滨理工大学校园网络信息中心,哈尔滨,150080 |
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| 摘 要： | 现有的入侵检测算法存在小样本情况下泛化能力差的问题。提出了利用核主成分分析和支持向量机结合进行入侵检测的方法。与传统算法相比，该方法对网络异常连接有很高的检测率、更强的泛化能力和更快的处理时间。最后在KDD CUP99数据集上进行的实验，证明了方法的适用性和高效性。
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| 关 键 词： | 核主成分分析 支持向量机 入侵检测 |
| 文章编号： | 1009-2552（2007）07-0029-03 |
| 修稿时间： | 2007-03-13 |
A multi - classification intrusion detection system based on KPCA and SVM |
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| SUN Zong-bao,SUN Ming-song. A multi - classification intrusion detection system based on KPCA and SVM[J]. Information Technology, 2007, 31(7): 29-31 |
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| Authors: | SUN Zong-bao SUN Ming-song |
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| Affiliation: | Network Information Center, Harbin University of Science and Technology, Harbin 150080, China |
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| Abstract: | Current IDS has poor generalization abihty as given less priority knowledge. The KPCA and SVM are adopted to implement intrusion detection. Compared with traditional algorithms this method can achieve higher detection rate and better generalization, and decrease time of performance. In the end of the paper the experiment on KDD CUP99 data set shows the effectiveness and excellent performance of the method. |
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| Keywords: | KPCA SVM intrusion detection |
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