Abstract
In recent years, there have been many methods proposed to perform network traffic classification based on application protocols. Still, there is a pressing need for a practical tool to benchmark the performance of these approaches in real-world high-performance network environments. In this paper, based on rigorous requirements analysis on real-world environments, we present a real-time traffic classification benchmark system, termed TrafficS, which aims at easy performance-evaluation between different intelligent methods. TrafficS is not only extensible to incorporate multiple traffic classification engines but supports different packet/stream sampling techniques as well. Furthermore, it could provide users a comprehensive means to perceive the difference between inspected methods in various aspects.
Keywords
- Network traffic classification
- high-performance network
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Yan, X., Liang, B., Ban, T., Guo, S., Wang, L. (2012). TrafficS: A Behavior-Based Network Traffic Classification Benchmark System with Traffic Sampling Functionality. In: Huang, T., Zeng, Z., Li, C., Leung, C.S. (eds) Neural Information Processing. ICONIP 2012. Lecture Notes in Computer Science, vol 7666. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34478-7_13
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DOI: https://doi.org/10.1007/978-3-642-34478-7_13
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-34477-0
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