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Recently traffic identification based on Machine Learning (ML) techniques has attracted a great deal of interest. Two challenging issues for these methods are how to deal with encrypted flows and cope with the rapid growing number of new application types correctly and early. We propose a hybrid traffic identification method and a novel unsupervised clustering algorithm, On-Line Density Based Spatial...
Recently traffic classifications based on statistics methods and machine learning techniques have attracted a great deal of interest. Some challenging issues for these methods are that most of them need prior analysis to detect traffic applications and training data sets to generate classification model offline; some require a high amount computation and memory resource. These are infeasible to cope...
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