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

Improving signature detection classification model using features selection based on customized features

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)
Othman, Z.A. ; Fac. of Inf. Sci. & Technol., Univ. Kebangsaan Malaysia (UKM), Bangi, Malaysia ; Bakar, A.A. ; Etubal, I.

Having an accurate Signature Detection Classification (SDC) Model has become highly demanding for Intrusion Detection Systems (IDS) to secure networks, especially when dealing with large and complex security audit data set. Selecting appropriate network features is one of the factors that influence the accuracy of SDC model. Past research has shown that the Hidden Marcov Chain, Genetic Algorithm, and the two-second time windows are among the best features selection methods for SDC Model. However this paper aims to improve the accuracy model by applying the features extraction based customized features. The customized features are the network data set which has been preprocessed through the following steps: removing biased attributes, discretized using chi-merge and remove the attributes with string value. The previous research applies the feature extraction based on all features. The best model is measured based on the detection rate, false alarm rate and number of rules using four data mining techniques such as Ripper(Jrip), Ridor, PART and decision three. The experiment is conducted using three random KDD-cup99 data sets. The result shows that the features extraction based on customized features has increased the accuracy model between 0.4% to 9% detection rates and reduced between 0.17% to 0.5% false alarm rates. The result shows the importance of data preprocessing in producing a high quality SDC Model.

Published in:

Intelligent Systems Design and Applications (ISDA), 2010 10th International Conference on

Date of Conference:

Nov. 29 2010-Dec. 1 2010

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.