Method for Getting Inter-Independent Features Used to Intrusion Detection System in Controllable and Trusted Networks

Author

Chen, Yu Sheng ; Li, Zhang ; Lin, Lu Xiu ; Fu, Liu Quan ; Wei, Xu Long ; Guo, Lei Yu

Author_Institution

Comput. Sci. Dept., North China Univ. of Sci. & Technol., Beijing, China

Volume

4

fYear

2009

fDate

19-21 May 2009

Firstpage

461

Lastpage

465

Abstract

In order to improve the reality, useful and whole performance of network intrusion detection system (IDS), the solution to get independent features used to (or evidence) IDS is presented in the paper. The approach of auto-recognition of inter-relativity of the features is developed, which is classification method for features. The features picked up by the method are used as the input of back propagate neural network (BPNN). The features are inter-independent, or weak relative. On the base of the chosen features, an IDS is built. Tests show that the approach and IDS developed in the article is useful and available. It is conclusion that a set of inter-independent features should be provided for IDS. The inter-relative degree of features can be required with the help of the method developed in the article.

Keywords

backpropagation; computer networks; security of data; auto-recognition; back propagate neural network; controllable networks; inter-independent features; inter-relativity; intrusion detection system; trusted networks; Application software; Availability; Computer security; Control systems; Information security; Intelligent networks; Intelligent systems; Intrusion detection; Neural networks; Testing; Back propagate neural network (BPNN); Inter-relativity of feature; Intrusion Detection System (IDS);

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Systems, 2009. GCIS '09. WRI Global Congress on

Conference_Location

Xiamen

Print_ISBN

978-0-7695-3571-5

Type

conf

DOI

10.1109/GCIS.2009.393

Filename

5209250