SELECTION OF ONLINE NETWORK TRAFFIC DISCRIMINATORS FOR ON-THE-FLY TRAFFIC CLASSIFICATION

  • Angela María Vargas Arcila Universidad del Cauca
  • Dr. Universidad del Cauca
  • Alvaro Rendon Gallon Universidad del Cauca
  • Dra. Universidad Carlos III de Madrid
Keywords: network traffic classification, traffic feature selection, online classification, incremental learning, machine learning

Abstract

There are several techniques to select a set of traffic features to perform traffic classification. However, most studies ignore the domain knowledge where traffic analysis or classification is performed and do not consider that the information carried in the networks is always moving. This paper describes a selection process of online network traffic discriminators, obtaining 24 traffic features that can be processed on the fly, and proposes them as a set of base attributes for future domain aware online analysis, processing or classification. For the selection of a set of traffic features, and to avoid inconveniences mentioned, we carried out three steps. The first step is a context knowledge based manual selection of traffic attributes that meet the condition of being obtained on the fly of the flow. The second step is focused on the quality analysis of previously selected attributes to ensure the relevance of each one when performing a traffic classification. In the third step, the usefulness of the discriminators in online traffic classification processes is verified by implementing several incremental learning algorithms.

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Published
2020-06-23
How to Cite
Vargas Arcila, A. M., Corrales Muñoz, J. C., Rendon Gallon, A., & Sanchis, A. (2020). SELECTION OF ONLINE NETWORK TRAFFIC DISCRIMINATORS FOR ON-THE-FLY TRAFFIC CLASSIFICATION. Revista Ingenierías Universidad De Medellín, 20(38). Retrieved from https://revistas.udem.edu.co/index.php/ingenierias/article/view/3009

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