Ensemble classifier for traffic in presence of changing distributions

Runxin Wang, Lei Shi, Brendan Jennings

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

3 Citations (Scopus)

Abstract

Traffic classification plays an important role in many short to medium term network management tasks and in long term network dimensioning/planning. In recent years a number of traffic classifiers have been proposed, in particular classifiers based on machine learning techniques exhibit high levels of accuracy. However, in practice, even if classifiers can be accurately trained at a given time, their accuracy will subsequently degrade when the characteristics of the network traffic change. In this paper, we propose an adjustable traffic classification system, the key technique of which is ensemble classification, assisted with a change detection method. Our system enables a traffic classifier to be effectively updated in response to the changing traffic distributions. Experimental results show that our classifier produces improved accuracy with relatively shorter updating time.

Original languageEnglish
Title of host publication2013 IEEE Symposium on Computers and Communications, ISCC 2013
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages629-635
Number of pages7
ISBN (Print)9781479937554
DOIs
Publication statusPublished - 2013
Event18th IEEE Symposium on Computers and Communications, ISCC 2013 - Split, Croatia
Duration: 07 Jul 201310 Jul 2013

Publication series

NameProceedings - International Symposium on Computers and Communications
ISSN (Print)1530-1346

Conference

Conference18th IEEE Symposium on Computers and Communications, ISCC 2013
Country/TerritoryCroatia
CitySplit
Period07/07/201310/07/2013

Keywords

  • Machine Learning
  • Traffic Classification

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