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Network protocol identification ensemble with EA optimization

Published: 06 July 2013 Publication History

Abstract

In computer networks, the ability to correctly classify and control traffic flows is essential in order to manage network resources. A number of works have focused on the identification of flow attributes, or discriminators, able to distinguish the underlying application protocol of a flow at an early stage of it's existence. In this study k-means is investigated for identifying distinct application protocols present within flow data sets generated using a select number of discriminators. The clusters identified were used in a supervised training process that correctly identified protocols with an almost perfect (99% percent) success rate.

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R. G. Goss and G. S. Nitschke. Automated network application classification: A competitive learning approach. In In, Proceedings of the IEEE Symposium Series on Computational Intelligence (IEEE SSCI 2013), 2013.
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cover image ACM Conferences
GECCO '13 Companion: Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
July 2013
1798 pages
ISBN:9781450319645
DOI:10.1145/2464576
  • Editor:
  • Christian Blum,
  • General Chair:
  • Enrique Alba
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 06 July 2013

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Author Tags

  1. artificial neural networks
  2. evolutionary algorithms
  3. network traffic classification

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GECCO '13
Sponsor:
GECCO '13: Genetic and Evolutionary Computation Conference
July 6 - 10, 2013
Amsterdam, The Netherlands

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Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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