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Traffic classification has wide applications in network management, from security monitoring to quality of service measurements. Recent research tends to apply machine learning techniques to flow statistical feature based classification methods. The nearest neighbor (NN)-based method has exhibited superior classification performance. It also has several important advantages, such as no requirements...
Identifying applications and classifying network traffic flows according to their source applications are critical for a broad range of network activities. Such classifications can be based on information derived from packet header fields and payload content, or statistical characteristics of flows and communication patterns of hosts. However, most of present methods rely on some forms of priori knowledge...
Traditional application identification based on port numbers has become increasingly inaccurate. A more accurate alternative is to inspect the application payloads of traffic flows. The main drawback of such method is that target applications must be manually analyzed beforehand. Another alternative is to exploit the distinctive statistical properties of traffic flows and apply machine learning techniques...
A number of recent works have proposed using data mining and machine learning techniques to classify traffic flows based on statistical flow characteristics. Most of these classifiers work offline, since full-flow statistics are not available until a flow is finished. Therefore, it is usually too late to take actions for online deployment. In this paper, we propose a simple and effective technique...
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....
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