Encryption Traffic Classification Method Based on AHP-CNN
To address the insufficient feature extraction of existing methods for encrypted traffic,this study proposes an en-crypted 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 pool-ing 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 extrac-tion 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 traf-fic,contributing to advancements in research and applications in the field of network security.
deep learningencrypted traffic classificationconvolutional neural networkhybrid poolingself-attention mechanism
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