| 基于KPCA和SVM的网络入侵检测 |
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| 引用本文： | 包潘晴,杨明福.基于KPCA和SVM的网络入侵检测[J].计算机应用与软件,2006,23(2):125-127. |
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| 作者姓名： | 包潘晴 杨明福 |
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| 作者单位： | 华东理工大学计算机科学系,上海,200237 |
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| 摘 要： | 入侵检测系统是网络安全的重要组成部分。目前的入侵检测算法存在着先验知识少的情况下推广能力差的问题。本文提出了利用核主成分分析（KPCA）和支持向量机（SVM）相结合进行入侵检测的方法。与传统算法相比，本文的方法对网络异常连接有更高的检测率和更强的泛化能力。文章最后在KDDCUP数据集上进行的实验，证明了本文方法的适用性和高效性。
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| 关 键 词： | 入侵检测 统计学习 核主成分分析 支持向量机 |
| 收稿时间： | 08 10 2004 12:00AM |
| 修稿时间： | 2004-08-10 |
INTRUSION DETECTION BASED ON KPCA AND SVM |
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| Bao Panqing,Yang Mingfu.INTRUSION DETECTION BASED ON KPCA AND SVM[J].Computer Applications and Software,2006,23(2):125-127. |
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| Authors: | Bao Panqing Yang Mingfu |
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| Affiliation: | Dept. of Computer Science, East China University of Science and Technology, Shanghai 200237, China |
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| Abstract: | IDS is an important part of network security. Current IDS has poor generalization ability when given less priority knowledge. In this paper,the KPCA and SVM are adopted to implement intrusion detection. Compared with traditional algorithms,this method can achieve higher detection rate and better generalization. At the end of the paper,the experiment on KDDCUP data set shows the effectiveness and excellent performance of our method. |
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| Keywords: | Intrusion detection Statistical learning KPCA SVM |
| 本文献已被 CNKI 维普 万方数据 等数据库收录！ |
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