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A. Moore and Zuev D., "Internet Traffic Classification Using Bayesian Analysis Techniques", in ACM SIGMETRICS 2005, Jun 2005.
Internet Traffic Classification Using Bayesian Analysis Techniques
Authors: A. Moore
Zuev D.
Published: ACM SIGMETRICS, 2005
URL:http://www.cl.cam.ac.uk/~awm22/publications/moore2005internet.pdf
Entry Date: 2009-02-06
Abstract: Accurate traffic classification is of fundamental importance to numerous other network activities, from security monitoring to accounting, and from Quality #of Service to providing operators with useful forecasts for long-term provisioning. We apply a Naive Bayes estimator to categorize traffic by application. Uniquely, our work capitalizes on hand-classified network data, using it as input to a supervised Naive Bayes estimator. In this paper we illustrate the high level of accuracy achievable with the Naive Bayes estimator. We further illustrate the improved accuracy of refined variants of this estimatortions annotations
Results:
  • Genome Campus Datasets
  • # Navie Bayes estimator; acheive 65% accuracy on per-flow classficationl with refinements, improve to 95%;
  • Analysis tools: WEKA
  Last Modified: Wed Mar-27-2019 22:23:20 PDT
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