## 4 Experimental Evaluation
### Ablation Experiments and Results  
For the sake of determining the validity of each component of CLSTM-MT, we conducted ablation experiments to verify the impact of each component of CLSTM-MT on the final model performance. To clearly present the importance of each component, we provide specific ablation experiment results. For each ablation experiment, we modified the configuration of the CLSTM-MT model by removing one component at a time while keeping the other components unchanged.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{4}{c}{**ISCXVPN2016**} & \multicolumn{4}{c}{**ISCXTOR2016**} \\ \cline{2-9}  & **Accuracy** & **Precision** & **Recall** & **F1-Score** & **Accuracy** & **Precision** & **Recall** & **F1-Score** \\ \hline FlowPrint & 0.6163 & 0.6697 & 0.6651 & 0.6673 & 0.5155 & 0.6778 & 0.6120 & 0.6337 \\ AppScanner & 0.6266 & 0.4864 & 0.5198 & 0.5030 & 0.5369 & 0.6920 & 0.6276 & 0.6582 \\ SVM & 0.6767 & 0.5152 & 0.5153 & 0.5150 & 0.6097 & 0.7190 & 0.5898 & 0.6480 \\ MT-CNN & 0.9329 & 0.9492 & 0.9173 & 0.9330 & 0.8124 & 0.9108 & 0.7538 & 0.8099 \\ CLSTM-MT & 0.9784 & 0.9788 & 0.9784 & 0.9783 & 0.8633 & 0.6778 & 0.7684 & 0.8336 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison with other advanced methods.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{4}{c}{**ISCXVPN2016**} & \multicolumn{4}{c}{**ISCXTOR2016**} & \multirow{2}{*}{**FLOPs**} & \multirow{2}{*}{**Params**} \\ \cline{2-2} \cline{4-11}  & **1\%** & **5\%** & **10\%** & **15\%** & **20\%** & **1\%** & **5\%** & **10\%** & **15\%** & **20\%** & **(10\%)** & **(10\%)** \\ \hline
1-conv CNN & 16.8\% & 31.5\% & 47.6\% & 49.8\% & 52.9\% & 23.2\% & 27.1\% & 35.7\% & 41.3\% & 51.2\% & 285.7 & 5.7 \\
2-conv CNN & 27.2\% & 33.6\% & 50.8\% & 52.6\% & 54.1\% & 26.7\% & 30.3\% & 42.9\% & 47.5\% & 53.8\% & 65.3 & 5.2 \\
3-conv CNN & 26.7\% & 33.9\% & 46.6\% & 50.4\% & 53.2\% & 21.8\% & 31.2\% & 33.2\% & 45.6\% & 51.7\% & 93.2 & 8.5 \\ BiLSTM & 30.3\% & 41.2\% & 50.7\% & 51.1\% & 58.2\% & 24.2\% & 34.4\% & 38.8\% & 42.7\% & 55.4\% & 63.8 & 4.7 \\
2-conv CNN + BiLSTM & 32.4\% & 43.3\% & 52.9\% & 55.2\% & 62.7\% & 31.3\% & 37.6\% & 43.9\% & 51.0\% & 56.6\% & 71.9 & 10.9 \\ CLSTM-MT & 62.1\% & 80.3\% & 83.9\% & 93.4\% & 97.8\% & 41.0\% & 61.5\% & 73.4\% & 81.2\% & 86.3\% & 34.9 & 4.1 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The performance of each model under different proportions of labeled traffic.  
According to the experimental results in Table 5, when we remove each component one by one, the model performance shows a significant decrease:  
* Without 2-conv CNN: If the CNN model is removed and only BiLSTM and the Mean Teacher architecture are used, the model accuracy drops to 85.6% and 80.4%. This indicates that the local information captured during the feature extraction phase by CNN is critical to the overall performance of the model.
* Without BiLSTM: When BiLSTM is removed and only CNN and the Mean Teacher architecture are used, the model accuracy is 91.2% and 82.2%. This shows that BiLSTM plays an important role in modeling the traffic sequence information.
* Without the Mean Teacher architecture: Without the Mean Teacher semi-supervised learning architecture, the model accuracy significantly decreases to 62.7% and 56.6%. This suggests that the Mean Teacher architecture can effectively leverage unlabeled data for learning, thus significantly improving model performance.