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In this paper, the authors introduce a novel framework for traffic identification that employs machine learning techniques focusing on the estimation of multiple traffic influencing factors. The effect of these factors is handled with the training of several machine learning models. They utilize the outcome of the multiple models via a recombination algorithm to achieve high overall true positive and true negative and low overall false positive and false negative classification ratio. The proposed method can improve the performance of every kind of machine learning based traffic identification engine making them capable of efficient operation in changing network environment i.e., when the probing node is trained and tested in different sites.
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