PTNet:A Semi-supervised Parallel Transformer Network for Encrypted Traffic Classification
With the widespread use of network encryption protocols,traditional network traffic classification technology has been challenged.The current method has the following limitations:first,the model is highly dependent on the depth feature,which requires the labeled training data set to be large enough in scale,otherwise the model will have difficulty generalizing to new data;second,the model only focuses on one modal feature of traffic,and the feature differentiation of the same mode of traffic from differ-ent categories may not be obvious.To solve these problems,a deep learning-based encryption traffic classification model called Par-allel Transformer Net(PTNet)is proposed in this paper.Based on the semi-supervised idea of pre-training and fine-tuning,the mod-el makes full use of a large amount of unlabeled traffic data on the network for pre-training,and then fine-tunes on the basis of a small amount of labeled data.Additionally,the model extracts the flow characteristics of load and packet length sequences in parallel to carry out multi-mode feature fusion.Three different traffic classification tasks and their corresponding datasets(Android,USTC-TFC,and CSTNET-TLS1.3)show good results,with classification accuracies reaching 95%,98%,and 97%,respectively.
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