Robust Network Traffic Classification
Open accessAbstract
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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RTC can achieve higher flow and byte accuracy of up to 15% and 10%, respectively, compared to the second best technique, even with small labeled training datasets.
“Therefore, even with small labeled training datasets, RTC can achieve a higher flow and byte accuracy of up to 15% and 10%, respectively, in comparison to the second best technique.”
From A comprehensive survey on machine learning for networking: Evolution, applications and research opportunities (2018) · cites this paper as [497]
Joint application of supervised and unsupervised ML for traffic classification has demonstrated success.
“Consequentially, joint application of supervised and unsupervised ML for traffic classification [137, 497] has demonstrated success.”
From A comprehensive survey on machine learning for networking: Evolution, applications and research opportunities (2018) · cites this paper as [497]
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- Authors
- Zhang, J., Chen, X., Xiang, Y., Zhou, W., & Wu, J.
- Affiliations
- Deakin University (AU)
- Temple University (US)
- Venue
- IEEE/ACM Transactions on Networking
- Published
- 2015-08
- Volume
- 23
- Issue
- 4
- Pages
- 1257-1270
- ISSN
- 1063-6692 (ISSN-L)
- 1558-2566
- 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 · 2014: 1
- FWCI
- 18.61
- Type
- journal article
- Language
- en
- Open access
- green
- figshare.com
- 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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