Robust network traffic classification

Zhang, Jun, Chen, Xiao, Xiang, Yang, Zhou, Wanlei and Wu, Jie 2015, Robust network traffic classification, IEEE/ACM transactions on networking, vol. 23, no. 4, pp. 1257-1270, doi: 10.1109/TNET.2014.2320577.

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Title Robust network traffic classification
Author(s) Zhang, JunORCID iD for Zhang, Jun orcid.org/0000-0002-2189-7801
Chen, Xiao
Xiang, YangORCID iD for Xiang, Yang orcid.org/0000-0001-5252-0831
Zhou, WanleiORCID iD for Zhou, Wanlei orcid.org/0000-0002-1680-2521
Wu, Jie
Journal name IEEE/ACM transactions on networking
Volume number 23
Issue number 4
Start page 1257
End page 1270
Total pages 14
Publisher IEEE
Place of publication Piscataway, N.J.
Publication date 2015-08
ISSN 1063-6692
Keyword(s) Science & Technology
Technology
Computer Science, Hardware & Architecture
Computer Science, Theory & Methods
Engineering, Electrical & Electronic
Telecommunications
Computer Science
Engineering
Semi-supervised learning
traffic classification
zero-day applications
IDENTIFICATION
CLASSIFIERS
Summary As a fundamental tool for network management and security, traffic classification has attracted increasing attention in recent years. A significant challenge to the robustness of classification performance comes from zero-day applications previously unknown in traffic classification systems. In this paper, we propose a new scheme of Robust statistical Traffic Classification (RTC) by combining supervised and unsupervised machine learning techniques to meet this challenge. The proposed RTC scheme has the capability of identifying the traffic of zero-day applications as well as accurately discriminating predefined application classes. In addition, we develop a new method for automating the RTC scheme parameters optimization process. The empirical study on real-world traffic data confirms the effectiveness of the proposed scheme. When zero-day applications are present, the classification performance of the new scheme is significantly better than four state-of-the-art methods: random forest, correlation-based classification, semi-supervised clustering, and one-class SVM.
Language eng
DOI 10.1109/TNET.2014.2320577
Field of Research 0805 Distributed Computing
080503 Networking and Communications
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category C1 Refereed article in a scholarly journal
ERA Research output type C Journal article
Copyright notice ©2015, IEEE
Persistent URL http://hdl.handle.net/10536/DRO/DU:30078921

Document type: Journal Article
Collections: School of Information Technology
2018 ERA Submission
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