# QUIC Network Traffic Classification Using Ensemble Machine Learning Techniques
## 6 Discussion  
In this section, the performance sensitivity of different techniques is analyzed based on the number of packets used for training each model. As such, we consider both the holdout training method and the cross-validation method. Please note that the sensitivity analysis is based on only the accuracy metric. The details are visualized in Figures 5-7. If we first analyze the performance sensitivity of the RF algorithm in light of the number of packets used, it is observed that RF can classify QUIC traffic almost with the same accuracy for input packets 15, 30, and 40. This observation is about the holdout strategy and the same observation is also true for cross-validation scenarios. These trends can be visualized in Figures 6 and 7. When more than 50 packets are used as input features, the classification accuracy remarkably increases. In the case of ET, the classification trend is slightly different; for the holdout strategy, the classification accuracy almost remains stable \begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Ref.** & **Year** & **Dataset** & **Algorithms** & **Main Objective** & **Score** \\ \hline
[9] & 2022 & \begin{tabular}{l} Orange’20, UC Davis \\ QUIC [5] \\ \end{tabular} & \begin{tabular}{l} CNN and Stacked \\ LSTM \\ \end{tabular} & \
\begin{tabular}{l} Encrypted web \\ traffic classification \\ \end{tabular} &
\begin{tabular}{l} 99.37\% \\ (Acc.) \\ \end{tabular} \\ \hline
[10] & 2022 & \begin{tabular}{l} Orange’20, \\ UC Davis QUIC [5], \\ ISCX \\ \end{tabular} & ResNet34 & \
\begin{tabular}{l} Encrypted network \\ traffic classification \\ \end{tabular} &
\begin{tabular}{l} 99.24\% \\ (Acc.) \\ \end{tabular} \\ \hline
[8] & 2020 & \begin{tabular}{l} UC Davis \\ QUIC [5] \\ \end{tabular} & CNN & \begin{tabular}{l} Network traffic \\ classification \\ \end{tabular} & \begin{tabular}{l} 90.00\% \\ (Acc.) \\ \end{tabular} \\ \hline Proposed work & \begin{tabular}{l} UC Davis \\ QUIC [5] \\ \end{tabular} & \begin{tabular}{l} **RF, ET GBT, XGBT,** \\ **LGBM** \\ \end{tabular} & \
\begin{tabular}{l} Encrypted traffic \\ classification \\ \end{tabular} &
\begin{tabular}{l} **99.40** \\ (Acc.) \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Comparison between the proposed work and others in the literature. Figure 6: Performance sensitivity analysis of all models in holdout strategy. Figure 7: Performance sensitivity analysis of all models in cross-validation strategy. till 60 packets, and, after that significant increase is observed, and in the cross-validation scenario, classification accuracy increases with 40 packets afterward. GBT can classify QUIC traffic with varying accuracy depending on the number of packets. This behavior is seen up to 60 packets and, after that, classification accuracy increases as seen in Figure 6. However, when cross-validation is used for training, GBT shows trending classification accuracy as seen in Figure 7. Additionally, it should be noted that RF and GBT are going shoulder-to-shoulder in the cross-validation strategy, which shows the robustness of the cross-validation training strategy in QUIC traffic classification. For XGBT and LGBM, in both holdout and cross-validation scenarios, the scores of accuracies are very close and show similar trending as seen in Figures 6 and 7. As seen in these two figures, these techniques are less sensitive to input packets (features) compared to other techniques for both training strategies. More specifically, the performance sensitivity is less, concerning the number of input features for XGBT and LGBM in the case of cross-validation scenarios. One of the reasons that XGBT and LGBM outperform the others is the fact that they are built as a boosting technique. While bagging and stacking try to enhance the predictions by fitting multiple models at the same time without a difference between the models, the boosting technique involves fitting the models sequentially such that the next model will be more enhanced than the previous model.