Cited by 391Internet Traffic Analysis and Secure E-votingNetwork Security and Intrusion DetectionSoftware-Defined Networks and 5G

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Robust Network Traffic Classification

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Zhang, Jun, Chen, Xiao, Xiang, Yang, Zhou, Wanlei, Wu, Jie·2015·IEEE/ACM Transactions on Networking

DOI 10.1109/tnet.2014.2320577

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Abstract

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.

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Authors: Zhang, J., Chen, X., Xiang, Y., Zhou, W., & Wu, J.
Affiliations: Deakin University (AU)
Temple University (US)
ORCID: Zhang, Jun
Chen, Xiao
Xiang, Yang
Zhou, Wanlei
Wu, Jie
Venue: IEEE/ACM Transactions on Networking
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Published: 2015-08
Volume: 23
Issue: 4
Pages: 1257-1270
ISSN: 1063-6692 (ISSN-L)
1558-2566
DOI: 10.1109/tnet.2014.2320577
OpenAlex: W2161251616
Cited by: 391
References: 56
Citations by year: 2026: 15 · 2025: 26 · 2024: 22 · 2023: 34 · 2022: 38 · 2021: 48 · 2020: 58 · 2019: 54 · 2018: 40 · 2017: 30 · 2016: 18 · 2015: 7
FWCI: 18.61
Type: journal article
Language: en
Open access: green
figshare.com
Licence: cc-by
ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html (vor)
Locations: IEEE/ACM Transactions on Networking (publishedVersion)
Figshare (submittedVersion, cc-by)
Swinburne figshare (Swinburne University of Technology) (submittedVersion)
Topics: Physical Sciences › Computer Science › Artificial Intelligence › Internet Traffic Analysis and Secure E-voting
Physical Sciences › Computer Science › Computer Networks and Communications › Network Security and Intrusion Detection
Physical Sciences › Computer Science › Computer Networks and Communications › Software-Defined Networks and 5G
Keywords: network traffic classification, hybrid supervised and unsupervised learning, application identification, parameter optimization, network security, network management

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