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An Efficient Approach for Encrypted Traffic Classification using CNN and Bidirectional GRU

With the rapid development of the Internet, the amount and types of network traffic have increased dramatically in tandem. Therefore, precise network traffic classification has become an essential aspect of network management. Furthermore, as user privacy and data encryption requirements have grown, more encrypted traffic has evolved. Because of the wide variety of encryption techniques and methodologies, network traffic categorization has become extremely challenging. Traditional traffic classification methods have been unable to meet the demand for classification accuracy. In this study, we propose a network structure that combines CNN and Bi-GRU to learn the temporal and spatial features of encrypted traffic data. We utilize the public dataset ISCX VPN-non VPN to evaluate the effect of our model, and we use accuracy, recall, and F1 score as criteria. Finally, our model had a classification accuracy of93.1 %, with a recall rate of 93.7% and an F1 score of 93.6%. We also discussed about the differences between Bi-GRU and LSTM in terms of model parameter scale and time efficiency. Experiments show that Bi-GRU has the greatest classification effect and efficiency.