By Topic

Intrusion detection technique by using fuzzy ART on computer network security

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

2 Author(s)
Preecha Somwang ; Faculty of Information Science, Nakhon Ratchasima College, Thailand ; Woraphon Lilakiatsakun

The problem of computer network security is the very hard of detecting new attacks which do not have known signatures of intrusion. Intrusion Detection Systems (IDS) is a program of monitoring the events in a computer network and analyzing them for signature of intrusions. This paper proposed the clustering technique by using hybrid method based on Principal Component Analysis (PCA) and Fuzzy Adaptive Resonance Theory (FART) for identifying various attacks. The PCA is applied to random selects the best attribution and reduction the feature space. FART is implementing used to classifying difference group of data, Normal and Anomalous. The results show that the proposed technique can improve the high performance of the detection rate and to minimize the false alarm rate. The evaluated our approach on the benchmark data from KDDCup'99 data set.

Published in:

2012 7th IEEE Conference on Industrial Electronics and Applications (ICIEA)

Date of Conference:

18-20 July 2012