Intrusion Detection using An Ensemble of Support Vector Machines

被引：5

作者：

Kumar, G. Kishor ^{[1
]}

Kumar, R. Raja ^{[1
]}

Basha, M. Suleman ^{[1
]}

Reddy, K. Nageswara ^{[1
]}

机构：

[1] RGMCET, Dept CSE, Nandyal, India

来源：

JOURNAL OF MECHANICS OF CONTINUA AND MATHEMATICAL SCIENCES | 2019年

关键词：

Bootstrapping; classification; svm; ensemble techniques; intrusion detection;

D O I：

10.26782/jmcms.spl.3/2019.09.00020

中图分类号：

O3 [力学];

学科分类号：

08 ; 0801 ;

摘要：

This paper "an ensemble of Support Vector Machines (SVM)" for network-based intrusion detection. Bootstrapping is applied to derive various training sets from the given training set. Then a SVM is derived for each training set. The decisions of all SVMS is taken and majority voting is considered to classify the given query pattern as a normal or an anomalous one. We have shown the results of applying an ensemble of Support Vector Machines to the two standard data sets ,viz., 1999 KDDCupandCreditcarddatasets.

引用

页码：266 / 275

页数：10

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