A survey of techniques for internet traffic classification using machine learning
Open accessAbstract
The research community has begun looking for IP traffic classification techniques that do not rely on `well known' TCP or UDP port numbers, or interpreting the contents of packet payloads. New work is emerging on the use of statistical traffic characteristics to assist in the identification and classification process. This survey paper looks at emerging research into the application of Machine Learning (ML) techniques to IP traffic classification - an inter-disciplinary blend of IP networking and data mining techniques. We provide context and motivation for the application of ML techniques to IP traffic classification, and review 18 significant works that cover the dominant period from 2004 to early 2007. These works are categorized and reviewed according to their choice of ML strategies and primary contributions to the literature. We also discuss a number of key requirements for the employment of ML-based traffic classifiers in operational IP networks, and qualitatively critique the extent to which the reviewed works meet these requirements. Open issues and challenges in the field are also discussed.
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There are various surveys on machine learning in networking, but they are specialized to specific areas or specific network technologies.
“Though there are various surveys on ML in networking [18,61,82,142,246,339], this survey is purposefully different.”
From A comprehensive survey on machine learning for networking: Evolution, applications and research opportunities (2018) · cites this paper as [339]
Nguyen and Armitage, though impactful, is now dated and only addresses traffic classification in networking.
“For instance, Nguyen and Armitage [339], though impactful, is now dated and only addresses traffic classification in networking.”
From A comprehensive survey on machine learning for networking: Evolution, applications and research opportunities (2018) · cites this paper as [339]
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- Authors
- Nguyen, T. T. T., & Armitage, G.
- Affiliations
- Swinburne University of Technology (AU)
- ORCID
- Armitage, Grenville
- Venue
- IEEE Communications Surveys & Tutorials
- Published
- 2008
- Volume
- 10
- Issue
- 4
- Pages
- 56-76
- ISSN
- 1553-877X (ISSN-L)
- OpenAlex
- W2096118443
- Cited by
- 1,664
- References
- 67
- Citations by year
- 2026: 70 · 2025: 85 · 2024: 60 · 2023: 87 · 2022: 101 · 2021: 117 · 2020: 163 · 2019: 138 · 2018: 145 · 2017: 110 · 2016: 92 · 2015: 82 · 2014: 85 · 2013: 97 · 2012: 79
- FWCI
- 79.66
- Type
- journal article
- Language
- en
- Open access
- green
- figshare.com
- Locations
- IEEE Communications Surveys & Tutorials (publishedVersion)
- Figshare (submittedVersion, cc-by)
- Swinburne figshare (Swinburne University of Technology) (submittedVersion)
- Swinburne figshare (Swinburne University of Technology)
- 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 › Hardware and Architecture › Network Packet Processing and Optimization
- Keywords
- network traffic classification, machine learning, network traffic monitoring, data mining
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