A streaming flow-based technique for traffic classification applied to 12 + 1 years of Internet traffic

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• 作者：Valentín Carela-Español ; Pere Barlet-Ros ; Albert Bifet…
• 关键词：Traffic classification ; Machine learning ; Stream classification ; Hoeffding adaptive tree ; Network monitoring
• 刊名：Telecommunication Systems
• 出版年：2016
• 出版时间：October 2016
• 年：2016
• 卷：63
• 期：2
• 页码：191-204
• 全文大小：1,518 KB
• 刊物类别：Business and Economics
• 刊物主题：Economics
  Business Information Systems
  Computer Communication Networks
  Artificial Intelligence and Robotics
  Probability Theory and Stochastic Processes
  
• 出版者：Springer Netherlands
• ISSN：1572-9451
• 卷排序：63

文摘

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.