Abstract: Abstract: To address the insufficient feature extraction of existing methods for encrypted traffic， this study proposes an encrypted traffic classification method based on an Attention-based Hybrid Pooling Convolutional Neural Network （AHP-CNN）. This method improves the pooling layers of Convolutional Neural Networks （CNNs） by combining average pooling and max pooling in a parallel manner， forming a dual-layer synchronized pooling pattern. This enables the capturing of both global and local features of network encrypted traffic. Furthermore， a self-attention module is incorporated into the model to enhance the extraction of dependency relationships among encrypted traffic features， leading to more accurate classification. Experimental results demonstrate a significant improvement in the accuracy of encrypted traffic identification using the proposed model， with an F1 score exceeding 0.94. This research provides a more effective and precise approach for the classification of network encrypted traffic， contributing to advancements in research and applications in the field of network security.

Key words: Key words: deep learning, encrypted traffic classification, convolutional neural network, hybrid pooling, self-attention mechanism