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Traffic identification in big internet data

Wang, Binfeng, Zhang, Jun, Zhang, Zili, Luo, Wei and Xia, Dawen 2016, Traffic identification in big internet data. In Yu, Shui and Guo, Song (ed), Big data concepts, theories, and applications, Springer, Berlin, Germany, pp.129-156, doi: 10.1007/978-3-319-27763-9_3.

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Title Traffic identification in big internet data
Author(s) Wang, Binfeng
Zhang, JunORCID iD for Zhang, Jun orcid.org/0000-0002-2189-7801
Zhang, ZiliORCID iD for Zhang, Zili orcid.org/0000-0002-8721-9333
Luo, WeiORCID iD for Luo, Wei orcid.org/0000-0002-4711-7543
Xia, Dawen
Title of book Big data concepts, theories, and applications
Editor(s) Yu, ShuiORCID iD for Yu, Shui orcid.org/0000-0003-4485-6743
Guo, Song
Publication date 2016
Chapter number 3
Total chapters 12
Start page 129
End page 156
Total pages 28
Publisher Springer
Place of Publication Berlin, Germany
Summary The era of big data brings new challenges to the network traffic technique that is an essential tool for network management and security. To deal with the problems of dynamic ports and encrypted payload in traditional port-based and payload-basedmethods, the state-of-the-art method employs flow statistical features and machine learning techniques to identify network traffic. This chapter reviews the statistical-feature based traffic classification methods, that have been proposed in the last decade. We also examine a new problem: unclean traffic in the training stage of machine learning due to the labeling mistake and complex composition of big Internet data. This chapter further evaluates the performance of typical machine learning algorithms with unclean training data. The review and the empirical study can provide a guide for academia and practitioners in choosing proper traffic classification methods in real-world scenarios.
ISBN 9783319277639
9783319277615
Language eng
DOI 10.1007/978-3-319-27763-9_3
Field of Research 080503 Networking and Communications
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category B1 Book chapter
ERA Research output type B Book chapter
Copyright notice ©2016, Springer International Publishing Switzerland
Persistent URL http://hdl.handle.net/10536/DRO/DU:30085302

Document type: Book Chapter
Collection: School of Information Technology
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