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Network traffic classification plays an important role in various network activities. Due to the ineffectiveness of traditional port-based and payload-based methods, recent works proposed using machine learning methods to classify flows based on statistical characteristics. In this paper, the authors present a comprehensive evaluation of the effectiveness of these statistical methods for real-time traffic classification problem. They evaluate three different flow feature sets that are used to capture distinct properties of each application, two of them consist of features generated from full flows and the third is made up of early sub-flow statistics derived from the first few packets of each flow.
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