Classification of correlated internet traffic flows

Zhang, Jun, Chen, Chao, Xiang, Yang and Zhou, Wanlei 2012, Classification of correlated internet traffic flows, in TrustCom 2012 : Proceedings of the 11th IEEE International Conference on Trust, Security and Privacy in Computing and Communications, IEEE, Piscataway, N. J., pp. 490-496.

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Title Classification of correlated internet traffic flows
Author(s) Zhang, Jun
Chen, Chao
Xiang, Yang
Zhou, Wanlei
Conference name IEEE International Conference on Trust, Security and Privacy in Computing and Communications (11th : 2012 : Liverpool, England)
Conference location Liverpool, England
Conference dates 25-27 Jun. 2012
Title of proceedings TrustCom 2012 : Proceedings of the 11th IEEE International Conference on Trust, Security and Privacy in Computing and Communications
Editor(s) Min, Geyong
Wu, Yulei
Lei, Liu (Chris)
Jin, Xiaolong
Jarvis, Stephen
Al-Dubai, Ahmed Y.
Publication date 2012
Conference series IEEE International Conference on Trust, Security and Privacy in Computing and Communications
Start page 490
End page 496
Total pages 7
Publisher IEEE
Place of publication Piscataway, N. J.
Keyword(s) traffic classification
network security
naive Bayes
Summary A critical problem for Internet traffic classification is how to obtain a high-performance statistical feature based classifier using a small set of training data. The solutions to this problem are essential to deal with the encrypted applications and the new emerging applications. In this paper, we propose a new Naive Bayes (NB) based classification scheme to tackle this problem, which utilizes two recent research findings, feature discretization and flow correlation. A new bag-of-flow (BoF) model is firstly introduced to describe the correlated flows and it leads to a new BoF-based traffic classification problem. We cast the BoF-based traffic classification as a specific classifier combination problem and theoretically analyze the classification benefit from flow aggregation. A number of combination methods are also formulated and used to aggregate the NB predictions of the correlated flows. Finally, we carry out a number of experiments on a large scale real-world network dataset. The experimental results show that the proposed scheme can achieve significantly higher classification accuracy and much faster classification speed with comparison to the state-of-the-art traffic classification methods.
ISBN 9781467321723
9780769547459
Language eng
Field of Research 080503 Networking and Communications
080109 Pattern Recognition and Data Mining
Socio Economic Objective 890199 Communication Networks and Services not elsewhere classified
HERDC Research category E1 Full written paper - refereed
Copyright notice ©2012, IEEE
Persistent URL http://hdl.handle.net/10536/DRO/DU:30049566

Document type: Conference Paper
Collection: School of Information Technology
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