An Incremental SVM for Intrusion Detection Based on Key Feature Selection

被引：1

作者：

Xia, Yong-Xiang ^{[1
]}

Shi, Zhi-Cai ^{[1
]}

Hu, Zhi-Hua ^{[2
]}

机构：

[1] Shanghai Univ Engn Sci, Elect & Elect Engn Inst, Shanghai, Peoples R China

[2] Shanghai Maritime Univ, Logist Res Ctr, Shanghai, Peoples R China

来源：

2009 THIRD INTERNATIONAL SYMPOSIUM ON INTELLIGENT INFORMATION TECHNOLOGY APPLICATION, VOL 3, PROCEEDINGS | 2009年

关键词：

Intrusion detection; SVM; Incremental SVM; Network security; Classification; NEURAL-NETWORKS; MODEL;

D O I：

10.1109/IITA.2009.358

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Proposed a method of detecting intrusion using incremental SVM based on key feature selection. A center SVM summarizes the distributed samples and incorporates them to build the incremental SVM for locals. By eliminating the redulldant features of sample dataset, the space dimension of the sample data is reduced. Using This method, it can overcome the shortages of SVM-time-consuming of training and massive dataset storage. The simulation experiments with KDD Cup 1999 data demonstrate that our proposed method achieves the increasing performance for intrusion detection.

引用

页码：205 / +

页数：2

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