By Topic

Two Level Anomaly Detection Classifier

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

2 Author(s)
Khan, A. ; Sch. of Comput., Dublin City Univ., Dublin ; Khan, S.

This paper proposes two-level strategy for building the anomaly detection classifier, namely, macro level and micro level classification. The former intend to classify network data on a broader perspective to predict whether it is normal or a potential attack. The later classifies individual anomalies within each category of known attacks. The paper also investigates various feature selection techniques for choosing relevant features and study its effect on the performance of the anomaly detection classifiers. Experiments suggest that employing feature selection along with the proposed approach give anomaly detection rate of up to 99%.

Published in:

Computer and Electrical Engineering, 2008. ICCEE 2008. International Conference on

Date of Conference:

20-22 Dec. 2008