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Statistics-based Internet traffic classification using machine learning techniques has attracted extensive research interest lately, because of the increasing ineffectiveness of traditional port-based and payload-based approaches. In particular, unsupervised learning, that is, traffic clustering, is very important in real-life applications, where labeled training data are difficult to obtain and new...
The recent years have seen extensive work on statistics-based network traffic classification using machine learning (ML) techniques. In the particular scenario of learning from unlabeled traffic data, some classic unsupervised clustering algorithms (e.g. K-Means and EM) have been applied but the reported results are unsatisfactory in terms of low accuracy. This paper presents a novel approach for...
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...
This paper presents a new method for the mining the hottest topics on Chinese Web page which is based on the improved k-means partitioning algorithm. The dictionary applied to word segmentation is reduced by deleting words is which are useless for clustering, and the dictionary tree is created to be applied to word segmentation. Then the speed of word segmentation is improved. Correspondence between...
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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