By Topic

An intrusion detection system using principal component analysis and time delay 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

6 Author(s)
Byoung-Doo Kang ; Dept of Comput. Eng., lnje Univ., Gyeongnam, South Korea ; Jae-Won Lee ; Jong-Ho Kim ; O-Hwa Kwon
more authors

The intrusion detection system (IDS) generally uses the misuse detection model based on rules because this model has low false alarm rates. However, the rule based IDSs are not efficient for mutated attacks, because they need additional rules for the variations of the attacks. In this paper, we propose an intrusion detection system using the principal component analysis (PCA) and the time delay neural network (TDNN). Packets on the network can be considered as gray images of which pixels represent bytes of the packets. From these continuous packet images, we extract principal components. And these components are used as an input of a TDNN classifier that discriminates between normal and abnormal packet flows. The system deals well with various mutated attacks, as well as well known attacks.

Published in:

Proceedings of 7th International Workshop on Enterprise networking and Computing in Healthcare Industry, 2005. HEALTHCOM 2005.

Date of Conference:

23-25 June 2005