By Topic

Detecting and Tracing Traffic Volume Anomalies in SINET3 Backbone Network

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

5 Author(s)
Ping Du ; Nat. Inst. of Inf., Tokyo ; Abe, S. ; Yusheng Ji ; Sato, S.
more authors

Traffic volume anomalies refer to apparent abrupt changes in time series of traffic volume, which can be propagate through the network. Detecting and tracing anomalies is a critical and difficult task for network operators. In this paper, we first propose a traffic decomposition method, which decomposes the traffic into three components: trend component, autoregressive (AR) component, and noise component. A traffic volume anomaly is detected when the AR component is out of prediction band for multiple links simultaneously. Then, the anomaly is traced using the projection of the detection result matrices for the observed links which are selected by a shortest-path-first algorithm. Finally we validate our detection and tracing method by using traffic data of the third-generation Science Information Network (SINET3) and show the detected and traced results.

Published in:

Communications, 2008. ICC '08. IEEE International Conference on

Date of Conference:

19-23 May 2008