Intrusion Detection Based on Support Vector Machine Using Heuristic Genetic Algorithm

Author

Tao Yerong ; Sui Sai ; Xie Ke ; Liu Zhe

Author_Institution

China Luoyang Electron. Equip. Test Center, Luoyang, China

fYear

2014

fDate

7-9 April 2014

Firstpage

681

Lastpage

684

Abstract

The parameters of Support Vector Machine (SVM) are optimized using heuristic genetic algorithm and then to detect the network intrusion behavior. The heuristic real-coded genetic algorithm is used to optimize the best parameters of SVM with Gauss kernel aimed at the classification accuracy of the model. The classification accuracy is largely improved. Experimental results show that this method has a broad application future.

Keywords

computer network security; genetic algorithms; pattern classification; support vector machines; Gauss kernel; SVM; classification accuracy; heuristic genetic algorithm; intrusion detection; network intrusion behavior; support vector machine; Accuracy; Classification algorithms; Genetic algorithms; Intrusion detection; Optimization; Support vector machines; Training; genetic algorithm; intrusion detection; support vector machines;

fLanguage

English

Publisher

ieee

Conference_Titel

Communication Systems and Network Technologies (CSNT), 2014 Fourth International Conference on

Conference_Location

Bhopal

Print_ISBN

978-1-4799-3069-2

Type

conf

DOI

10.1109/CSNT.2014.143

Filename

6821485

Link To Document

https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=144549