CA-STCNN: An Attention-based Hybrid Deep Learning Model for Encrypted Traffic Classification

With the increasing trend of network traffic encryption, both normal and abnormal traffic have begun to utilize encryption techniques to conceal their behavior, making accurate identification of encrypted traffic an essential prerequisite for network management and security. Current traffic classification methods generally suffer from inadequate feature extraction and lower multi-classification accuracy. This paper proposes a multiclassification hybrid deep learning model for encrypted traffic, called channel attention-based spatiotemporal convolutional neural network (CA-STCNN). The model extracts spatial features by convolutional neural network (CNN) and then introduces temporal convolutional neural network (TCN) to extract the temporal features. Using the convolution structure with more stable gradients can ensure the robustness of spatiotemporal feature extraction. Furthermore, the model also introduces the channel attention mechanism to assign weights to features in the channel dimension according to their contributions to classification. In the multi-classification experiments conducted using the ISCXVPN dataset, CA-STCNN outperforms existing methods in terms of accuracy, precision, recall, and F1 score.