By Topic

Effective metric for detecting distributed denial-of-service attacks based on information divergence

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 $33
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)
K. Li ; School of Engineering and Information Technology, Deakin University, Melbourne, Australia ; W. Zhou ; S. Yu

In information theory, the relative entropy (or information divergence or information distance) quantifies the difference between information flows with various probability distributions. In this study, the authors first resolve the asymmetric property of Renyi divergence and Kullback-Leibler divergence and convert the divergence measures into proper metrics. Then the authors propose an effective metric to detect distributed denial-of-service attacks effectively using the Renyi divergence to measure the difference between legitimate flows and attack flows in a network. With the proposed metric, the authors can obtain the optimal detection sensitivity and the optimal information distance between attack flows and legitimate flows by adjusting the orderacutes value of the Renyi divergence. The experimental results show that the proposed metric can clearly enlarge the adjudication distance, therefore it not only can detect attacks early but also can reduce the false positive rate sharply compared with the use of the traditional Kullback-Leibler divergence and distance approaches.

Published in:

IET Communications  (Volume:3 ,  Issue: 12 )