## 1 Introduction  
Network traffic has become an indispensable part of modern society. For the purpose of protecting user's privacy and data security, traffic encryption technology has been universally used in data transmission. However, this also presents many new challenges for network traffic identification. Factors such as the diversity of encryption algorithms [1],data security and privacy, and dynamically changing traffic patterns have increased the difficulty of identifying encrypted traffic.  
Nowadays, mainstream encryption traffic identification methods [2] mainly include feature-based methods, machine-learning-based methods, and deep-learning-based methods. Feature-based methods rely on the manual selection and manual extraction of explicit features. Although this method is intuitive and easy to implement, its performance is easily affected by changes in the encryption algorithm, resulting in low recognition accuracy. Machine-learning-based methods can automatically learn the characteristics of traffic from data to decrease the dependence on specific features and improve the flexibility of the model, but they still face the problem of complex model training and the high expense of data labeling when dealing with encrypted traffic. However, deep-learning-based methods count heavily on the manual annotation of data during model training. In particular, when new encryption protocols or algorithms are encountered, the robustness and adaptability of existing models are often insufficient, and they cannot respond to new situations quickly and effectively. Therefore, there is an urgent need to develop a new method with high detection accuracy and low data annotation cost, as well as strong robustness and adaptability, to accurately identify encrypted application traffic.  
The purpose of this method is to propose a cryptographic application traffic identification method based on CLSTM (a combination of CNN and BiLSTM) and Mean Teacher collaborative learning, aiming at the following.  
Improve recognition accuracy: The strong feature extraction capabilities of CNN and BiLSTM, combined with the advantages of Mean Teacher in semi-supervised learning, are leveraged to enhance recognition accuracy.  
Enhance robustness and adaptability: The consistency constraint on unlabeled data is used to improve the robustness and accuracy of the model to identify different types of encrypted traffic.  
Reduce the cost of traffic labeling: Under the Mean Teacher framework, the overall training cost of the model is reduced by using partially labeled data and mostly unlabeled data to train the model. To achieve these objectives, we designed the following scheme:  
Dataset preparation: Collect and preprocess encrypted traffic data from publicly available datasets.  
CLSTM model design: Construct a feature extraction model combining CNN and BiLSTM.  
Integration of the Mean Teacher framework: Integrate the Mean Teacher framework into the CLSTM model.  
Experimental validation: Experiments were designed to compare the effect of existing methods, and the effectiveness of the method is verified by ablation experiments.  
Through the above methods, we aim to propose a novel approach that excels in the area of encrypted traffic identification, not only improving the accuracy of traffic recognition but also maintaining good robustness and adaptability while reducing the cost of traffic data labeling.  
The rest part of this article is organized as follows. In the second section, we analyze and summarize the relevant work in the area of encrypted traffic classification. In the third section, we have introduced the system structure of CLSTM-MT and the data preprocessing module and its classification process. In the fourth part, through the detailed introduction of the whole experiment environment, the dataset used in the experiment, and the evaluation indicators of the experiment, we carried out the evaluation and visual analysis of the experimental outcomes. Finally, the thesis is summarized in Section 5.