By Topic

Anomaly intrusion detection using multi-objective genetic fuzzy system and agent-based evolutionary computation framework

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

3 Author(s)
Chi-Ho Tsang ; Dept. of Comput. Sci., Hong Kong City Univ., Kowloon, China ; Sam Kwong ; Hanli Wang

In this paper, we present a multi-objective genetic fuzzy system for anomaly intrusion detection. The proposed system extracts accurate and interpret able fuzzy rule-based knowledge from network data using an agent-based evolutionary computation framework. The experimental results on KDD-Cup99 intrusion detection benchmark data demonstrate that our system can achieve high detection rate for intrusion attacks and low false positive rate for normal network traffic.

Published in:

Fifth IEEE International Conference on Data Mining (ICDM'05)

Date of Conference:

27-30 Nov. 2005