By Topic

Application of Network Intrusion Detection Based on Fuzzy C-Means Clustering Algorithm

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
$31 $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)
Wuling Ren ; Coll. of Comput. & Inf. Eng., Zhejiang Gongshang Univ., Hangzhou, China ; Jinzhu Cao ; Xianjie Wu

Aiming at the problem of higher false positive and missing report rate in network intrusion detection, an intrusion detection method based on clustering algorithm is proposed in this paper. This method applies Fuzzy C-means clustering Algorithm to the detection of network intrusion. Through the building of intrusion detection model, carries out fuzzy partition and the clustering of data, and this will detach normal data and attack data effectively. The experiment shows the feasibility and validity of Fuzzy C-means clustering algorithm.

Published in:

Intelligent Information Technology Application, 2009. IITA 2009. Third International Symposium on  (Volume:3 )

Date of Conference:

21-22 Nov. 2009