Machine Learning in Software Defined Networks: Data collection and traffic classification

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

92 Citations (Scopus)

Abstract

Software Defined Networks (SDNs) provides a separation between the control plane and the forwarding plane of networks. The software implementation of the control plane and the built in data collection mechanisms of the OpenFlow protocol promise to be excellent tools to implement Machine Learning (ML) network control applications. A first step in that direction is to understand the type of data that can be collected in SDNs and how information can be learned from that data. In this work we describe a simple architecture deployed in an enterprise network that gathers traffic data using the OpenFlow protocol. We present the data-sets that can be obtained and show how several ML techniques can be applied to it for traffic classification. The results indicate that high accuracy classification can be obtained with the data-sets using supervised learning.
Original languageEnglish
Title of host publication2016 IEEE 24th International Conference on Network Protocols (ICNP)
Place of PublicationNew York
PublisherIEEE
ISBN (Electronic)978-1-5090-3281-5
ISBN (Print)978-1-5090-3282-2
DOIs
Publication statusPublished - 14 Dec 2016
Event24th IEEE International Conference on Network Protocols (ICNP) - Singapore, Singapore
Duration: 8 Nov 201611 Nov 2016
Conference number: 24th

Publication series

NameIEEE International Conference on Network Protocols Proceedings
PublisherIEEE
Volume2016-December
ISSN (Print)1092-1648

Conference

Conference24th IEEE International Conference on Network Protocols (ICNP)
Abbreviated titleICNP
Country/TerritorySingapore
CitySingapore
Period8/11/1611/11/16

Keywords

  • Data Analysis
  • Machine Learning
  • Software defined Networks
  • Traffic Classification

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