| 引用本文: | 邹 涛,孙宏伟,田新广,等.网络入侵检测系统中的数据缩减技术.[J].国防科技大学学报,2003,25(6):16-20.[点击复制] |
| ZOU Tao,SUN Hongwei,TIAN Xinguang,et al.Data Reduction in Network Based on the Intrusion Detection System[J].Journal of National University of Defense Technology,2003,25(6):16-20[点击复制] |
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| 网络入侵检测系统中的数据缩减技术 |
| 邹 涛, 孙宏伟, 田新广, 张尔扬 |
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(国防科技大学 电子科学与工程学院，湖南 长沙 410073)
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| 摘要: |
| 在进行事件分析之前，网络入侵检测系统首先要面对数据缩减的问题。以ANIDS为背景，分析了两种重要的数据缩减技术：相关特征子集选择和特征再构造。提出了一种基于Wrapper方法的最优特征子集选取算法SRRW。在考虑学习算法偏置的情况下，通过识别强相关特征并引入约束，能够更快地搜索并获得最优的相关特征子集。从特征再构造角度出发实现数据缩减，并通过因子负荷量矩阵分析了原始特征之间的相关性。 |
| 关键词: 网络入侵检测 数据缩减 相关特征选取 主成分分析 |
| DOI： |
| 投稿日期：2003-05-13 |
| 基金项目: |
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| Data Reduction in Network Based on the Intrusion Detection System |
| ZOU Tao, SUN Hongwei, TIAN Xinguang, ZHANG Eryang |
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(College of Electronic Science and Engineering, National Univ. of Defense Technology, Changsha 410073, China)
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| Abstract: |
| NIDSs deal with the problem of data reduction before analyzing the events. Two important measures used in ANIDS are proposed: FSS and new feature construction. A novel algorithm named SRRW is put forward first, which can produce OFS by recognizing all strongly relevant features and restrict them in searching process. A feature construction method is used to get the OFS. The correlations between the original features can be analyzed by factor loading matrix. |
| Keywords: NIDS data reduction relevant feature selection PCA |
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