# Fast and Accurate Multi-Task Learning for Encrypted Network Traffic Classification
## 4 Evaluation, Result and Analysis
### Evaluation Result
#### 4.3.1 Performance of the Proposed Method  
To validate our proposed method, we performed experiments on three tasks. In task #1, classifying the encryption, the highest accuracy, precision, recall, and F1 score were 99.29%, 98.61%, 99.47%, and 99.03%, respectively. In task #2, classifying the category, the highest accuracy, precision, recall, and F1 score were 97.38%, 97.31%, 95.93%, and 96.61%, respectively. In task #3, classifying the application, the highest accuracy, precision, recall, and F1 score were 96.89%, 96.91%, 95.13%, and 96.01%, respectively.  
Figure 5 illustrates the confusion matrix detailing accuracy for each task. In subfigures (a), (b), and (c), the confusion matrix is presented for each task. In Figure 5, while the majority of classes within each task demonstrate a high accuracy exceeding 95%, AimChat and ICQChat in Figure 5c exhibit relatively lower accuracy. These applications, designed for online chatting and offering various services like voice and video calls, share common traits. However, the similarities between these applications make it difficult to distinguish traffic patterns accurately, leading to decreased classification accuracy. The intricacies of these chat applications contribute to the difficulty in achieving higher performance.  
Table 2 shows the best class segmentation results for evaluation performance by class within each task, and 50 epochs in total were performed for the experiment. Task #1 involves classifying two classes (i.e., VPN and Non-VPN), resulting in 98-99% accuracy, precision, recall, and F1 score. Task #2 involves categorizing traffic into six classes (i.e., Chat, Email, File Transfer, P2P, Streaming, VoIP). In task #2, the classes Email, P2P, Streaming, and VoIP are classified with 98-100% accuracy, while File Transfer and Chat are classified with relatively low accuracy of 95.44% and 94.86%. Task #3 involves categorizing traffic into sixteen classes (i.e., AimChat, Facebook, Hangout, ICQChat, Skype, Email, Gmail, FTP, SCP, SFITorrent, Netflix, Spotify, Vimeo YouTube, VoIPBuster). In Task #3, most classes were classified with 96-100% accuracy, with some relatively low accuracy results for certain classes such as Aim Chat, ICQ Chat, FTP, and SFIT.  
Figure 5: Confusion matrix for three tasks: (**a**) encapsulation task (two classes), (**b**) category task (six classes), (**c**) application task (sixteen classes).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Proposed Method**} \\ \hline
**Task** & **Class** & **Accuracy (\%)** & **Precision (\%)** & **Recall (\%)** & **F1-Score (\%)** \\ \hline \multirow{8}{*}{Task \#3: Application} & AimChat & 93.75 & 78.95 & 93.75 & 85.71 \\  & Facebook & 97.62 & 98.97 & 97.62 & 98.29 \\  & Hangout & 98.91 & 98.19 & 98.91 & 98.55 \\  & IOChat & 72.73 & 88.89 & 72.73 & 80.00 \\  & Skype & 99.61 & 99.12 & 99.61 & 99.36 \\  & Email & 97.96 & 98.97 & 97.96 & 98.46 \\  & Gmail & 96.30 & 89.66 & 96.30 & 92.86 \\  & FTP & 87.78 & 91.30 & 87.78 & 89.51 \\  & SCP & 99.76 & 100.00 & 99.76 & 99.88 \\  & SFTP & 92.31 & 100.00 & 92.31 & 96.00 \\  & BitTorrent & 100.00 & 100.00 & 100.00 & 100.00 \\  & Netflix & 97.67 & 100.00 & 97.68 & 98.82 \\  & Spotify & 95.16 & 95.16 & 95.16 & 95.16 \\  & Vimeo & 99.06 & 98.13 & 99.06 & 98.59 \\  & YouTube & 96.15 & 96.90 & 96.15 & 96.53 \\  & VoIPBuster & 94.01 & 96.90 & 96.15 & 92.88 \\ \hline \hline \end{tabular}
\end{table}
Table 2: _Cont._Figure 6 shows the learning curve for the three tasks in training and testing. In Figure 6, the losses represent the total losses for the three tasks, with the learning and testing losses gradually decreasing.