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Classifying network traffic is very challenging and is still an issue yet to be solved due to the increase of new applications and traffic encryption. In this paper, we propose a novel hybrid approach for the network flow classification, in which we first apply the payload signature based classifier to identify the flow applications and unknown flows are then identified by a decision tree based classifier...
Third generation (3G) wireless networks have been well studied and optimized with traditional radio resource management techniques, but still there is room for improvement. Cognitive radio (CR) technology can bring significant network improvements by providing awareness to the surrounding radio environment, exploiting previous network knowledge and optimizing the use of radio resources using machine...
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 study, we evaluate the effectiveness of machine learning techniques on the real-time traffic classification problem....
The identification of network applications is of fundamental important to numerous network activities. Unfortunately, traditional port-based classification and packet payload-based analysis exhibit a number of shortfalls. A promising alternative is to use Machine Learning (ML) techniques and identify network applications based on per-flow features. Since a lot of flow features can be used for flow...
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