## 4 Experimental Evaluation
### Experimental Results Compared to Baseline Models  
We used five baseline models to validate the performance of our work. 1-conv CNN: The 1-conv CNN method uses a one-dimensional convolutional layer to directly extract features from the processed NPY files and then classifies the encrypted traffic using these features. 2-conv CNN: The 2-conv CNN method is very similar to the 1-conv CNN,but it uses two convolutional layers and two pooling layers in the convolutional part to extract and pool features. 3-conv CNN: Unlike one-dimensional and two-dimensional convolutions, the 3-conv CNN uses three convolutional layers and three pooling layers in the feature extraction part. The 1-conv CNN and deeper convolutional networks (such as 2-conv CNN and 3-conv CNN) are mainly used in extracting local spatial features from traffic. By gradually increasing the number of convolutional and pooling layers, we are able to capture more local information in the data. BiLSTM: Additionally, we used a Bidirectional Long Short-Term Memory (BiLSTM) network to extract features from the sequential data of the traffic to evaluate the influence of sequential features on the experimental results. CNN-BiLSTM: By combining the CNN and BiLSTM networks, we fused the local features and sequential data features of the traffic to validate the effectiveness of this combined network in traffic data processing. Encrypted traffic data have strong temporal characteristics. BiLSTM can effectively model these temporal relationships through its bidirectional propagation mechanism, thereby extracting time-dependent information from the traffic data. We found that when using the BiLSTM model alone, the model can leverage the sequential data characteristics of the traffic to improve classification accuracy. This indicates that temporal features are crucial for the encrypted traffic classification task. With the count of CNN layers increasing, the model becomes more and more complex and has a higher ability to adapt to the training data. Nevertheless, this added complexity can result in overfitting, with the model learning noise and uncorrelated patterns from the training set, resulting in a poorer ability to generalize to unseen data. In our experiments, when we increased the number of CNN layers from two to three, the training accuracy improved slightly, but the validation accuracy dropped significantly. This shows that the model started to overfit the training data. This result is reasonable. Therefore, we selected the 2-conv CNN, which performed better in classification among the three convolutional models, as part of our base model algorithm. Furthermore, the BiLSTM network takes the traffic sequence data as the input feature of the model, and we observe a significant improvement in performance. Consequently, we chose the combined 2-conv CNN and BiLSTM network as the base model architecture for both the student and teacher models in the CLSTM-MT framework. The results in Table 3 show that the 2-conv CNN + BiLSTM model combined with Mean Teacher framework performs better than other baseline models under the same proportion of labeled data. This may be because the model structure can make more effective use of limited labeled data for feature extraction and learning. The Mean Teacher framework enhances the model's ability to utilize unlabeled data through consistent regularization and other ways so that better traffic classification results can be obtained under different proportions of labeled data. Moreover, our model can still maintain relatively high accuracy when labeling data at a relatively low proportion (such as less than 5%), which is of great significance for resource-limited application scenarios and can reduce the cost of data labeling while ensuring certain classification accuracy. Besides, on the \"ISCXVPN2016\" and \"ISCXTOR2016\" dataset, the accuracy of CLSTM-MT reaches as high as 97.84% and 86.3%.