By Topic

Apply anomaly grey forecasting algorithm to cyberspace situation prediction

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)
Weisong He ; Sch. of Commun. & Inf. Eng., Univ. of Electron. Sci. & Technol. of China, Chengdu ; Guangmin Hu ; Hongmei Xiang

In recent years, much research has been devoted to the cyberspace situation awareness; nevertheless, few have investigated the case that the network traffic data collected may include missing values and sufficient network traffic data may not be acquired for privacy protection or the limitation of network storage equipment capacity. Our focus in this position paper is on introducing an anomaly grey forecasting (AGF) method for cyberspace situation prediction under less data little sample, and the experiment with Abilene network Netflow data verify this method.

Published in:

Cybernetics and Intelligent Systems, 2008 IEEE Conference on

Date of Conference:

21-24 Sept. 2008