# Fast and Accurate Multi-Task Learning for Encrypted Network Traffic Classification
## 4 Evaluation, Result and Analysis
### Evaluation Result
#### 4.3.3 Performance of the Efficiency  
The proposed method utilizes MTL to perform multi-task classification on the ISCX VPN/Non-VPN 2016 dataset. The goal is to achieve high performance by simultaneously \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{5}{c}{**Comparison Results for Task \#3: Application**} \\ \hline
**Method** & **Accuracy (\%)** & **Precision (\%)** & **Recall (\%)** & **F1 Score (\%)** \\ \hline AppScanner [54] & 62.66 & 48.64 & 51.98 & 49.35 \\ CUMUL [44] & 53.65 & 41.29 & 45.35 & 42.36 \\ BIND [45] & 67.67 & 51.52 & 51.53 & 49.65 \\ DF [46] & 61.16 & 66.97 & 66.51 & 65.31 \\ FS-Net [15] & 66.47 & 48.19 & 48.48 & 47.37 \\ GraphDApp [38] & 62.28 & 59.00 & 54.72 & 55.58 \\ TSCRNN [17] & - & - & - & - \\ Deep Packet [10] & 97.58 & 97.85 & 97.45 & 97.65 \\
1D-CNN [26] & 86.60 & - & - & 86.50 \\ FastTraffic [39] & 92.24 & 93.58 & 92.84 & 93.12 \\ MATEC [47] & 69.21 & 73.32 & 65.40 & 68.24 \\ PERT [21] & 82.29 & 70.92 & 71.73 & 69.92 \\ ET-BERT (flow) [34] & 85.19 & 75.08 & 72.94 & 73.06 \\ ET-BERT (packet) [34] & 99.62 & 99.36 & 99.38 & 99.37 \\ XENTC [33] & 96.37 & - & - & 94.63 \\ BFCN [22] & 99.65 & 99.36 & 99.47 & 99.41 \\ YaTC [24] & - & - & - & - \\ Flow-MAE [25] & 99.87 & 99.91 & 99.89 & 99.90 \\ \hline Proposed & 96.89 & 96.91 & 95.13 & 96.01 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison results for task #3 in ISCX 2016 VPN/Non-VPN. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{5}{c}{**Comparison Results for Task \#2: Category**} \\ \hline
**Method** & **Accuracy (\%)** & **Precision (\%)** & **Recall (\%)** & **F1 Score (\%)** \\ \hline GraphDApp [38] & 59.77 & 60.45 & 62.20 & 60.36 \\ TSCRNN [17] & - & 92.70 & 92.60 & 92.60 \\ Deep Packet [10] & 93.29 & 93.77 & 93.06 & 93.21 \\
1D-CNN [26] & 98.30 & - & - & 98.60 \\ FastTraffic [39] & 94.50 & 94.77 & 94.26 & 94.40 \\ MATEC [47] & 73.20 & 84.43 & 82.40 & 82.87 \\ PERT [21] & 93.52 & 94.00 & 93.49 & 93.68 \\ ET-BERT (flow) [34] & 97.29 & 97.56 & 97.31 & 97.33 \\ ET-BERT (packet) [34] & 98.90 & 98.91 & 98.90 & 98.90 \\ XENTC [33] & 97.03 & - & - & 97.06 \\ BPCN [22] & 99.12 & 99.13 & 99.11 & 99.11 \\ YaTC [24] & 98.07 & - & - & 98.04 \\ Flow-MAE [25] & 99.15 & 99.24 & 99.15 & 99.17 \\ \hline Proposed & 97.38 & 97.31 & 95.93 & 96.61 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: _Cont._handling various classification tasks. The efficiency of the model refers to its ability to quickly adapt to downstream tasks. To evaluate this efficiency, we compared the proposed method with other approaches and measured the processing speed. However, the interpretation of the model's efficiency may vary depending on hardware performance and data. Therefore, maintaining the same experimental environment and dataset is crucial for a fair comparison. Since it is difficult to reproduce these conditions exactly, we compare our results to those presented in other studies [8; 33; 34; 39]. Table 5 shows the results on fine-tune efficiency evaluation. In Table 5, ST represents the processing time for one sample and PT represents the processing time for one packet. Among the four models, ET-BERT and XENTC are general classification models, while MATEC and FastTraffic are models designed for lightweight purposes. All of them perform single-task classification. From an ST perspective, ET-BERT yields a range of 8.30-9.61 ms. The lightweight models, MATEC and FastTraffic, yield 2.10 and 0.25 ms, respectively. The proposed method achieves higher efficiency than ET-BERT with an execution time of 1.27 ms, but it is less efficient than MATEC and FastTraffic. Nevertheless, considering the results in Tables 3 and 4, the proposed method demonstrates 4.65-27.68% higher accuracy compared to MATEC and FastTraffic. Furthermore, the proposed method is more efficient than MATEC as it can learn the three tasks simultaneously. From a PT perspective, ET-BERT yields 155.7 ms, XENTC produces 15.1 ms, MATEC results in 1.3 ms, and FastTraffic yields 0.59 ms.