KFDA and clustering based multiclass SVM for intrusion detection

doi:1005-8885 (2008) 01-0123-06

中国邮电高校学报(英文) ›› 2008, Vol. 15 ›› Issue (1): 123-128.doi: 1005-8885 (2008) 01-0123-06

• Artificial Intelligence • 上一篇

KFDA and clustering based multiclass SVM for intrusion detection

魏宇欣;武穆清

Institute of Communication Networks Intergrated Technique,
Beijing University of Posts and Telecommunications, Beijing 100876, China

收稿日期:2007-06-08 修回日期:1900-01-01 出版日期:2008-03-31
通讯作者: 魏宇欣

KFDA and clustering based multiclass SVM for intrusion detection

Institute of Communication Networks Intergrated Technique,
Beijing University of Posts and Telecommunications, Beijing 100876, China

Received:2007-06-08 Revised:1900-01-01 Online:2008-03-31
Contact: Wei Yuxin

摘要/Abstract

摘要：

To improve the classification accuracy and reduce the training time, an intrusion detection technology is proposed, which combines feature extraction technology and multiclass support vector machine (SVM) classification algorithm. The intrusion detection model setup has two phases. The first phase is to project the original training data into kernel fisher discriminant analysis (KFDA) space. The second phase is to use fuzzy clustering technology to cluster the projected data and construct the decision tree, based on the clustering results. The overall detection model is set up based on the decision tree. Results of the experiment using knowledge discovery and data mining (KDD) from 99 datasets demonstrate that the proposed technology can be an an effective way for intrusion detection.

关键词:

Intrusion;detection,;kernel;fisher;discriminant;analysis,;fuzzy;clustering,;support;vector;machine

Abstract:

Key words:

Intrusion detection;kernel fisher discriminant analysis;fuzzy clustering;support vector machine

中图分类号:

TP393.08

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KFDA and clustering based multiclass SVM for intrusion detection

KFDA and clustering based multiclass SVM for intrusion detection

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