Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Efficient Distribution-Derived Features for High-Speed Encrypted Flow Classification
Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science (from 2013). (DISCO)ORCID iD: 0000-0003-3461-7079
Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science (from 2013).
2018 (English)In: NetAI'18 Proceedings of the 2018 Workshop on Network Meets AI & ML, New York: Association for Computing Machinery (ACM), 2018, p. 21-27Conference paper, Published paper (Refereed)
Abstract [en]

Flow classification is an important tool to enable efficient network resource usage, support traffic engineering, and aid QoS mechanisms. As traffic is increasingly becoming encrypted by default, flow classification is turning towards the use of machine learning methods employing features that are also available for encrypted traffic. In this work we evaluate flow features that capture the distributional properties of in-flow per-packet metrics such as packet size and inter-arrival time. The characteristics of such distributions are often captured with general statistical measures such as standard deviation, variance, etc. We instead propose a Kolmogorov-Smirnov discretization (KSD) algorithm to perform histogram bin construction based on the distributional properties observed in the data. This allows for a richer, histogram based, representation which also requires less resources for feature computation than higher order statistical moments. A comprehensive evaluation using synthetic data from Gaussian and Beta mixtures show that the KSD approach provides Jensen-Shannon distance results surpassing those of uniform binning and probabilistic binning. An empirical evaluation using live traffic traces from a cellular network further shows that when coupled with a random forest classifier the KSD-constructed features improve classification performance compared to general statistical features based on higher order moments, or alternative bin placement approaches.

Place, publisher, year, edition, pages
New York: Association for Computing Machinery (ACM), 2018. p. 21-27
Keywords [en]
Traffic classification, Discretization, Machine learning
National Category
Computer Sciences
Research subject
Computer Science
Identifiers
URN: urn:nbn:se:kau:diva-68707DOI: 10.1145/3229543.3229548ISBN: 978-1-4503-5911-5 (electronic)OAI: oai:DiVA.org:kau-68707DiVA, id: diva2:1238764
Conference
2018 Workshop on Network Meets AI & ML. August 24 - 24, 2018. Budapest, Hungary.
Projects
HITSAvailable from: 2018-08-14 Created: 2018-08-14 Last updated: 2019-11-08Bibliographically approved

Open Access in DiVA

No full text in DiVA

Other links

Publisher's full textFulltextProgram med fulltext

Authority records BETA

Garcia, JohanKorhonen, Topi

Search in DiVA

By author/editor
Garcia, JohanKorhonen, Topi
By organisation
Department of Mathematics and Computer Science (from 2013)
Computer Sciences

Search outside of DiVA

GoogleGoogle Scholar

doi
isbn
urn-nbn

Altmetric score

doi
isbn
urn-nbn
Total: 179 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf