A streaming flow-based technique for traffic classification applied to 12 + 1 years of Internet traffic
The continuous evolution of Internet traffic and its applications makes the classification of network traffic a topic far from being completely solved. An essential problem in this field is that most of proposed techniques in the literature are based on a static view of the network traffic (i.e., th...
| Autores: | , , , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2016 |
| País: | España |
| Institución: | Universitat Politècnica de Catalunya (UPC) |
| Repositorio: | UPCommons. Portal del coneixement obert de la UPC |
| Idioma: | inglés |
| OAI Identifier: | oai:upcommons.upc.edu:2117/90717 |
| Acceso en línea: | https://hdl.handle.net/2117/90717 https://dx.doi.org/10.1007/s11235-015-0114-6 |
| Access Level: | acceso abierto |
| Palabra clave: | Machine learning Telecommunication -- Traffic -- Management Hoeffding adaptive tree Network monitoring Stream classification Traffic classification Aprenentatge automàtic Telecomunicació -- Tràfic -- Gestió Àrees temàtiques de la UPC::Informàtica::Arquitectura de computadors |
| Sumario: | The continuous evolution of Internet traffic and its applications makes the classification of network traffic a topic far from being completely solved. An essential problem in this field is that most of proposed techniques in the literature are based on a static view of the network traffic (i.e., they build a model or a set of patterns from a static, invariable dataset). However, very little work has addressed the practical limitations that arise when facing a more realistic scenario with an infinite, continuously evolving stream of network traffic flows. In this paper, we propose a streaming flow-based classification solution based on Hoeffding Adaptive Tree, a machine learning technique specifically designed for evolving data streams. The main novelty of our proposal is that it is able to automatically adapt to the continuous evolution of the network traffic without storing any traffic data. We apply our solution to a 12 + 1 year-long dataset from a transit link in Japan, and show that it can sustain a very high accuracy over the years, with significantly less cost and complexity than existing alternatives based on static learning algorithms, such as C4.5. |
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