| Summary: | Convolutional neural network based network traffic classification scheme suffers many disadvantages such as the complex structure designing, gradient declines or even explodes, the deterioration of prediction accuracy, and etc. A residual network based improved traffic classification algorithm is proposed. The convolution layer and pooling layer in the traditional convolutional neural network are replaced by the residual network layer, which can alleviate the problem that the traditional convolution network is too deep to train effectively. The data feature information learned by the proposed algorithm in the training stage is more comprehensive, and the trained model can also be more accurate. Simulation results show that the improved algorithm has higher accuracy than the traditional neural network, which can be improved from 92.05%to 96.18%.
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