Intrusion Detection by New Data Description Method

Author

GhasemiGol, Mohammad ; Monsefi, Reza ; Yazdi, Hadi Sadoghi

Author_Institution

Dept. of Comput. Eng., Ferdowsi Univ. of Mashhad (FUM), Mashhad, Iran

fYear

2010

fDate

27-29 Jan. 2010

Firstpage

1

Lastpage

5

Abstract

This paper presents a new approach in data description for intrusion detection based on Support Vector Data Description (SVDD). The SVDD is a well-known kernel method which tries to fit a hypersphere around the target objects and more precise boundary is depending on using proper kernel functions. In the proposed method we find a minimal hyperellipse around the normal objects to describe them. The overall experiments show prominence of our proposed method in comparison with the standard SVDD.

Keywords

pattern classification; security of data; support vector machines; intrusion detection; kernel method; one-class classification; support vector data description; Computational modeling; Computer simulation; Data engineering; Intelligent systems; Intrusion detection; Kernel; Principal component analysis; Reconstruction algorithms; Support vector machine classification; Support vector machines; Intrusion detection; One-class classification; Support vector data description;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Systems, Modelling and Simulation (ISMS), 2010 International Conference on

Conference_Location

Liverpool

Print_ISBN

978-1-4244-5984-1

Type

conf

DOI

10.1109/ISMS.2010.11

Filename

5416132