Intrusion detection Based on Fuzzy support vector machines

被引：3

作者：

Du Hongle ^{[1
]}

Teng Shaohua ^{[1
]}

Zhu Qingfang ^{[2
]}

机构：

[1] Guangdong Univ Technol, Fac Comp, Guangzhou, Guangdong, Peoples R China

[2] Luoyang Normal Univ, Coll Mat, Luoyang, Peoples R China

来源：

NSWCTC 2009: INTERNATIONAL CONFERENCE ON NETWORKS SECURITY, WIRELESS COMMUNICATIONS AND TRUSTED COMPUTING, VOL 2, PROCEEDINGS | 2009年

关键词：

Intrusion Detection; Support Vector Machine; Fuzzy membership function; membership functions;

D O I：

10.1109/NSWCTC.2009.276

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

A great deal of noise data in the Network connectivity information affect badly to build SVM optimal classification hyperplane and lead to higher classification error rate. In this paper, fuzzy membership function is applied into v-SVM; it acquires different values for each input data that accord to different effects on the classification result. Therefore different input samples points can make different contributions to the learning of the decision surface-the optimal separating hyperplane. Then the model of Intrusion Detection System based on SVM is presented, and detailedly illustrated the performance of this model. Finally, comparison of detection ability between v-SVM and v-FSVM is given. It is found that v-FSVM effectively reduce the impact of the noise data and improve the accuracy of decision-making.

引用

页码：639 / +

页数：2

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