The DOI matches its Crossref record; 52 of the 56 references it cites were found in a registry or an index.
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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“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]
In the citing paper
“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]
Citations
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A survey of techniques for internet traffic classification using machine learning
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4 of the 56 references couldn’t be matched to a known paper.
Research works printed but not linked: 2
- 2008, ?Cisco WAN and application optimization solution guide ?
- erman, 2006, Internet traffic identification using machine learning, Proc IEEE Global Telecommun Conf, 1
Cited by
Sorted by citations
Boutaba, R., Salahuddin, M. A., Limam, N., Ayoubi, S., Shahriar, N., Estrada-Solano, F., & Caicedo, O. M. (2018) · Journal of Internet Services and Applications · doi:10.1186/s13174-018-0087-2 · Cited by 1,036
Network Traffic Classifier With Convolutional and Recurrent Neural Networks for Internet of Things
López‐Martín, M., Carro, B., Sánchez-Esguevillas, A., & Lloret, J. (2017) · IEEE Access · doi:10.1109/access.2017.2747560 · Cited by 772
Deep Learning for Encrypted Traffic Classification: An Overview
Rezaei, S., & Liu, X. (2019) · IEEE Communications Magazine · doi:10.1109/mcom.2019.1800819 · Cited by 670
Machine Learning for Networking: Workflow, Advances and Opportunities
Wang, M., Cui, Y., Wang, X., Xiao, S., & Jiang, J. (2017) · IEEE Network · doi:10.1109/mnet.2017.1700200 · Cited by 448
Deep Learning Based Attack Detection for Cyber-Physical System Cybersecurity: A Survey
Zhang, J., Pan, L., Han, Q., Chen, C., Wen, S., & Xiang, Y. (2021) · IEEE/CAA Journal of Automatica Sinica · doi:10.1109/jas.2021.1004261 · Cited by 450
Self-citation
Metadata
- 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
- 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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