By Topic

A New Weighted Ensemble Model for Detecting DoS Attack Streams

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

5 Author(s)
Jinghua Yan ; Dept. of Comput. Sci., Beijing Univ. of Post & Telecommun., Beijing, China ; Xiaochun Yun ; Peng Zhang ; Jianlong Tan
more authors

Recently, DoS (Denial of Service) detection has become more and more important in web security. In this paper, we argue that DoS attack can be taken as continuous data streams, and thus can be detected by using stream data mining methods. More specifically, we propose a new Weighted Ensemble learning model to detect the DoS attacks. The Weighted Ensemble model first trains base classifiers using different data classification algorithms (i.e., decision tree, SVMs, and Naive Bayes) on multiple successive data chunks, and then weights each base classifier according to its prediction accuracy on the up-to-date data. Experimental results on the benchmark KDDCUP'99 dataset demonstrate that our new Weighted Ensemble model is able to successfully detect DoS attacks.

Published in:

Web Intelligence and Intelligent Agent Technology (WI-IAT), 2010 IEEE/WIC/ACM International Conference on  (Volume:3 )

Date of Conference:

Aug. 31 2010-Sept. 3 2010