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Obfuscated and encrypted protocols hinder traffic classification by classical techniques such as port analysis or deep packet inspection. Therefore, there is growing interest for classification algorithms based on statistical analysis of the length of the first packets of flows. Most classifiers proposed in literature are based on machine learning techniques and consider each flow independently of...
Traffic classification, a branch of passive network measurement, becomes more and more important for network management. As traditional traffic classification techniques like port-based and payload-based techniques become ineffective for complicated internet applications which use dynamic port number and encryption techniques to avoid detection, machine learning based techniques gained more and more...
Traditional traffic classification techniques like port-based and payload-based techniques are becoming ineffective owning to more and more Internet applications using dynamic port number and encryption techniques. Therefore, in the past few years, many researches have addressed machine learning-based techniques. Most researches of machine learning-based traffic identification use traffic samples...
P2P traffic has become one of the most significant portions of the network traffic. How to improve the accuracy of the traffic identification efficiently is still a difficult problem. A promising approach that has recently received some attention is traffic classification using machine learning techniques. In this paper, we propose a BP neural network algorithm for P2P traffic classification problem...
These years, P2P applications have multiplied, evolved and take a big part of Internet traffic workload. Identifying the P2P traffic and understanding their behavior is an important field. Some port, payload and transport layer feature based methods were proposed. P2P traffic identification methods by examining user payload or well-defined port numbers no longer adapt to current P2P applications....
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