Proceedings of the 2007 International Conference on Intelligent Systems and Knowledge Engineering (ISKE 2007)

SVM-based analysis and prediction on network traffic

Authors
Weidong Luo1, Xingwei Liu, Jian Zhang
1School of Mathematics & Computer Engineering, Xihua University
Corresponding Author
Weidong Luo
Available Online October 2007.
DOI
https://doi.org/10.2991/iske.2007.284How to use a DOI?
Keywords
Network traffic, Predictive model, LS-SVM, NS2
Abstract

With continuous scale-up of the network and increase of the kinds of the services on the network, more and more people pay attention to the modeling and prediction for network traffic. Recently, SVM (Support Vector Machine), a new machine learning method, is comprehensively used to solve the problem of non-liner classification and regression. A network traffic predictive method presented in this paper is based on the LS-SVM (Least Squares SVM). Using NS2 simulator, we simulate the process of the network running with Drop-tail and RED controller respectively, then collect the being predicted traffic data which is on the bottleneck router. The results on the precision of prediction is good and feasible

Copyright
© 2007, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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Volume Title
Proceedings of the 2007 International Conference on Intelligent Systems and Knowledge Engineering (ISKE 2007)
Series
Advances in Intelligent Systems Research
Publication Date
October 2007
ISBN
978-90-78677-04-8
ISSN
1951-6851
DOI
https://doi.org/10.2991/iske.2007.284How to use a DOI?
Copyright
© 2007, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

TY  - CONF
AU  - Weidong Luo
AU  - Xingwei Liu
AU  - Jian Zhang
PY  - 2007/10
DA  - 2007/10
TI  - SVM-based analysis and prediction on network traffic
BT  - Proceedings of the 2007 International Conference on Intelligent Systems and Knowledge Engineering (ISKE 2007)
PB  - Atlantis Press
SP  - 1657
EP  - 1660
SN  - 1951-6851
UR  - https://doi.org/10.2991/iske.2007.284
DO  - https://doi.org/10.2991/iske.2007.284
ID  - Luo2007/10
ER  -