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Classifying network traffic in the big data era

Xiang, Yang 2013, Classifying network traffic in the big data era, in INCOS 2013 : Proceedings of the 5th International Conference on Intelligent Networking and Collaborative Systems, IEEE Computer Society, Piscataway, N.J., pp. xxxi-xxxi, doi: 10.1109/INCoS.2013.9.

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Title Classifying network traffic in the big data era
Author(s) Xiang, YangORCID iD for Xiang, Yang
Conference name Intelligent Networking and Collaborative Systems. Conference (5th : 2013 : Xi'an, China)
Conference location Xi'an,China
Conference dates 9 -11 Sep. 2013
Title of proceedings INCOS 2013 : Proceedings of the 5th International Conference on Intelligent Networking and Collaborative Systems
Editor(s) Xhafa, Fatos
Barolli, Leonard
Chen, Xiaofeng
Publication date 2013
Conference series International Conference on Intelligent Networking and Collaborative Systems
Start page xxxi
End page xxxi
Publisher IEEE Computer Society
Place of publication Piscataway, N.J.
Summary With the arrival of Big Data Era, properly utilizing the power of big data is becoming increasingly essential for the strength and competitiveness of businesses and organizations. We are facing grand challenges from big data from different perspectives, such as processing, communication, security, and privacy. In this talk, we discuss the big data challenges in network traffic classification and our solutions to the challenges. The significance of the research lies in the fact that each year the network traffic increase exponentially on the current Internet. Traffic classification has wide applications in network management, from security monitoring to quality of service measurements. Recent research tends to apply machine-learning techniques to flow statistical feature based classification methods. In this talk, we propose a series of novel approaches for traffic classification, which can improve the classification performance effectively by incorporating correlated information into the classification process. We analyze the new classification approaches and their performance benefit from both theoretical and empirical perspectives. A large number of experiments are carried out on two real-world traffic datasets to validate the proposed approach. The results show the traffic classification performance can be improved significantly even under the extreme difficult circumstance of very few training samples. Our work has significant impact on security applications.
ISBN 9780769549880
Language eng
DOI 10.1109/INCoS.2013.9
Field of Research 080503 Networking and Communications
080501 Distributed and Grid Systems
Socio Economic Objective 890103 Mobile Data Networks and Services
HERDC Research category E3 Extract of paper
Copyright notice ©2013, IEEE
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Document type: Conference Paper
Collection: School of Information Technology
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