Optimizing radial basis function networks to recognize network attacks for intrusion detection

Wei Pan; Weihua Li; Haobin Shi; Jianfeng Yan

doi:10.1117/12.657359

4 January 2006 Optimizing radial basis function networks to recognize network attacks for intrusion detection

Wei Pan; Weihua Li; Haobin Shi; Jianfeng Yan

Author Affiliations +

Proceedings Volume 5985, International Conference on Space Information Technology; 59851V (2006) https://doi.org/10.1117/12.657359
Event: International Conference on Space information Technology, 2005, Wuhan, China

Abstract

A methodology for optimizing radial basis function (RBF) networks is proposed, which consists of the RBF network and the self-organizing map (SOM), aiming at improving the performance of the recognition and classification of novel attacks for intrusion detection. The optimal network architecture of the RBF network is determined automatically by the improved SOM algorithm, in which the centers and the number of hidden neurons are self-adjustable. The intrusion feature vectors are extracted from a benchmark dataset (the KDD-99) designed by DARPA. The experimental results demonstrate that the proposed approach to recognize network attacks performance especially in terms of both efficient and accuracy.

Citation Download Citation

Wei Pan, Wei Pan, Weihua Li, Weihua Li, Haobin Shi, Haobin Shi, Jianfeng Yan, Jianfeng Yan, } "Optimizing radial basis function networks to recognize network attacks for intrusion detection", Proc. SPIE 5985, International Conference on Space Information Technology, 59851V (4 January 2006); doi: 10.1117/12.657359; https://doi.org/10.1117/12.657359

ACCESS THE FULL ARTICLE

PERSONAL SIGN IN
Full access may be available with your subscription

Email or Username Forgot your username?

Password Forgot your password?

Show

Remember Email/Username on this computer

Remember Password

No SPIE account? Create an account
or Institutional Sign In via Shibboleth

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members: $14.40

Non-members: $18.00 ADD TO CART

Show All Keywords

Keywords/Phrases

Search In:

Publication Years