Application of Improved Support Vector Machines in Intrusion Detection

Author

Zhang, Yongli ; Zhu, Yanwei

Author_Institution

Dept. of Basic courses, He Bei Polytech. Univ., Tangshan, China

fYear

2010

fDate

22-23 May 2010

Firstpage

1

Lastpage

4

Abstract

Intrusion detection system is of most importance to network security. Support Vector Machine (SVM) is algorithm about how to solve machine learning problems under circumstance of small sample. The paper respectively applies SVM based on least square and least-square SVM improved by greedy algorithm to intrusion detection, and does simulation experiment on intrusions detection data. Experiment result shows that least-square SVM based on greedy algorithm is more suitable in intrusion detection system in circumstance that the prior knowledge is less.

Keywords

computer network security; greedy algorithms; learning (artificial intelligence); support vector machines; greedy algorithm; intrusion detection; least square SVM; machine learning problems; network security; support vector machines; Computer networks; Data security; Educational institutions; Greedy algorithms; Information security; Intrusion detection; Least squares methods; Machine learning; Support vector machine classification; Support vector machines;

fLanguage

English

Publisher

ieee

Conference_Titel

e-Business and Information System Security (EBISS), 2010 2nd International Conference on

Conference_Location

Wuhan

Print_ISBN

978-1-4244-5893-6

Electronic_ISBN

978-1-4244-5895-0

Type

conf

DOI

10.1109/EBISS.2010.5473653

Filename

5473653