Cart (Loading....) | Create Account
Close category search window
 

Dimension reduction using feature extraction methods for real-time misuse detection systems

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

4 Author(s)
Kuchimanchi, G.K. ; Dept. of Comput. Sci., Louisiana State Univ., Ruston, LA, USA ; Phoha, V.V. ; Balagani, K.S. ; Gaddam, S.R.

We present a novel signed gain in information (GI) measure for quantitative evaluation of gain or loss in information due to dimension reduction using feature extraction in misuse detection applications. GI is defined in terms of sensitivity mismatch measure (Φ) and specificity mismatch measure (⊗). 'Φ' quantifies information gain or loss in feature-extracted data as the change in detection accuracy of a misuse detection system when reduced data is used instead of untransformed original data. Similarly, '⊗' quantifies information gain or loss as the change in the number of false alarms generated by a misuse detection system when feature-extracted data is used instead of original data. We present two neural network methods for feature extraction: (1) NNPCA and (2) NLCA for reducing the 41-dimensional KDD Cup 1999 data. We compare our methods with principal component analysis (PCA). Our results show that the NLCA method reduces the test data to approximately 30% of its original size while maintaining a GI comparable to that of PCA and the NNPCA method reduces the test data to approximately 50% with GI measure greater than that of PCA.

Published in:

Information Assurance Workshop, 2004. Proceedings from the Fifth Annual IEEE SMC

Date of Conference:

10-11 June 2004

Need Help?


IEEE Advancing Technology for Humanity About IEEE Xplore | Contact | Help | Terms of Use | Nondiscrimination Policy | Site Map | Privacy & Opting Out of Cookies

A not-for-profit organization, IEEE is the world's largest professional association for the advancement of technology.
© Copyright 2014 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.