By Topic

Intrusion Detection Method Based on Wavelet Neural Network

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

4 Author(s)
Jianjing Sun ; Dept. of Autom. Control, Beijing Union Univ., Beijing ; Han Yang ; Jingwen Tian ; Fan Wu

Aimed at the intrusion behaviors are characterized with uncertainty, complexity, diversity and dynamic tendency and the advantages of wavelet neural network (WNN), an intrusion detection method based on WNN is presented in this paper. Moreover, we adopt a algorithm of reduce the number of the wavelet basic function by analysis the sparseness property of sample data which can optimize the wavelet network in a large extent, and the learning algorithm based on the gradient descent was used to train network. We discussed and analyzed the impact factor of intrusion behaviors. With the ability of strong nonlinear function approach and fast convergence rate of WNN, the intrusion detection method based on WNN can detect various intrusion behaviors rapidly and effectively by learning the typical intrusion characteristic information. The experimental result shows that this intrusion detection method is feasible and effective.

Published in:

Knowledge Discovery and Data Mining, 2009. WKDD 2009. Second International Workshop on

Date of Conference:

23-25 Jan. 2009