Online Internet Traffic Classification Based on Proximal SVM

Online and accurate traffic classification is a key challenge for network management. Internet traffic classification based on flow statistics using machine learning method has attracted great attention. To solve the drawback of the previous classification scheme to meet the requirements of the network activities, our work mainly focuses on how to build an online Internet traffic classification system based on flow statistics. We propose an online Internet traffic classification based on proximal SVM. Experiment results illustrate this method can classify online traffic with first p packet of flow with high accuracy. Meanwhile, the proximal SVM method is computationally more efficient than the previous SVM methods with similar accuracies.

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