Cited by 1,664Internet Traffic Analysis and Secure E-votingNetwork Security and Intrusion DetectionNetwork Packet Processing and Optimization

A survey of techniques for internet traffic classification using machine learning

Open access
Nguyen, Thuy T.T., Armitage, Grenville·2008·IEEE Communications Surveys & Tutorials

Abstract

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.

Integrity signals

Registry

Crossref · 10.1109/surv.2008.080406

Cites

67 of 67 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

We couldn’t retrieve this paper’s full text, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Cites

Flagged first, then by citations

Cited by

Sorted by citations

Authors
Nguyen, T. T. T., & Armitage, G.
Affiliations
Swinburne University of Technology (AU)
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
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

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review