## 5 Experimental Results and Analysis
\begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{3}{c}{**Mixed Traffic Scenarios (Basic)**} \\ \hline
**Traffic Scenario** & **Low-Latency** & **Accuracy (\%)\(\sim\)** \\ \hline A + B & NO & 89.7 \\ A + C & **YES** & **92.8** \\ B + C & **YES** & **94.2** \\ A + A & NO & 88.3 \\ B + B & NO & 90.6 \\ C + C & **YES** & **96.5** \\ \hline \hline \end{tabular} A = File Transfer, B = Video Streaming, C = Low-Latency.  
\end{table} 
Table 5: Basic traffic scenarios with accuracy. performs as the traffic patterns become more intricate and diverse. Table 6 presents the results of these experiments, showing the classification accuracy both with and without the application of wavelet transform (WT). The wavelet transform was employed to enhance feature extraction, particularly in the presence of low-latency traffic, which often exhibits patterns similar to noise in signal processing. The addition of wavelet transform consistently improved classification accuracy across all scenarios. For example, in Scenario 3C, where all traffic types were low-latency, the model achieved an accuracy of 93.2% with wavelet transform, compared to 88.2% without it. Similarly, in Scenario 6 (2A + C), where the traffic was composed of two instances of FTP and one instance of low-latency traffic, accuracy improved from 76.5% to 83.2% with the application of wavelet transform. As complexity increased, such as in scenarios with four types of traffic (e.g., Scenario 13: A + B + 2C), the model's performance remained strong but showed a slight decline, with an accuracy of 79.2% with wavelet transform. This decline reflects the increasing difficulty in distinguishing between different traffic types as the mix becomes more complex. Figures 12 and 13 further visualize the model's performance. The confusion matrix (Figure 12) displays the accuracy of the model in classifying low-latency traffic across the complex traffic scenarios. The diagonal dominance shows a strong correlation between predicted and actual classifications, with the model performing particularly well in distinguishing between low-latency and non-low-latency traffic. Scenarios where low-latency traffic was present (e.g., Scenario 3C) showed high classification accuracy. Furthermore, Figure 13 illustrates the accuracy improvement brought about by applying the wavelet transform in each scenario. The plot clearly shows a consistent increase in accuracy when wavelet transform is applied, particularly in scenarios with low-latency traffic, highlighting the robustness of our model under complex conditions. Also, Figure 14 provides a visual representation of the accuracy improvement across various scenarios. The improvement is evident, particularly in scenarios involving low-latency traffic, where the wavelet transform significantly enhances classification performance. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{5}{c}{**Mixed Traffic Scenarios (Complex)**} \\ \hline
**No** & **Scenario** & **Low-Latency** & **Acc (\%)\(\sim\)** & **Acc (\%)\(\sim\) with WT** \\ \hline
1 & 3A & NO & 82.9 & 86.8 \\
2 & 3B & NO & 83.6 & 88.2 \\
3 & 3C & **YES** & 88.2 & 93.2 \\
4 & 2A + B & NO & 77.2 & 82.1 \\
5 & A + 2B & NO & 71.1 & 77.4 \\
6 & 2A + C & **YES** & 76.5 & 83.2 \\
7 & A + 2C & **YES** & 79.6 & 83.8 \\
8 & 2B + C & **YES** & 77.0 & 84.1 \\
9 & B + 2C & **YES** & 80.7 & 84.4 \\
10 & A + B + C & **YES** & 72.9 & 78.2 \\
11 & 2A + B + C & **YES** & 68.8 & 74.2 \\
12 & A + 2B + C & **YES** & 69.7 & 75.3 \\
13 & A + B + 2C & **YES** & 72.5 & 79.2 \\ \hline \hline \end{tabular} A = File Transfer, B = Video Streaming, C = Low-Latency.  
\end{table} 
Table 6: Complex traffic scenarios with accuracy. Figure 14: Impact of wavelet transform on classification accuracy. Figure 12: Confusion matrix of complex scenarios. Figure 13: Impact of wavelet transform on classification accuracy in complex traffic scenarios. In the next experiment, our aim was to classify three distinct types of Internet traffic: FTP, video streaming, and low-latency traffic.