## 5 Experimental Results and Analysis
The dataset used for training the classification algorithm was designed to maintain a balanced representation of each traffic type, with approximately one hour's worth of sampling per category. The decision to utilize 14,400 samples for each traffic type was deliberate. This sampling frequency, equivalent to an average of four samples per second over the course of an hour, allowed for a comprehensive representation of the behavior of each traffic category. Table 7 presents the exact sample sizes for each traffic type, confirming the careful selection of our dataset, ensuring a balanced representation essential for robust model training and validation. The deliberate selection of FTP, video streaming, and low-latency traffic was made to encompass a diverse representation of common Internet activities while maintaining a focused scope for the experiment. The accuracy results for our classification model are illustrated in Figure 15, further supporting the exceptional performance with rates of 99.2% for FTP traffic, 99.3% for video streaming, and 99.4% for low-latency types, validating the effectiveness of our approach in accurately classifying these primary Internet traffic types. Figure 15 presents the confusion matrix, visualizing the performance evaluation of the classification of the proposed models among different types of traffic. The ROC curve, Figure 16, illustrates the model's performance in terms of true positive rates and false positive rates for each traffic type. The Area Under the Curve (AUC) values show that the model achieved excellent classification performance across all three classes, with AUC values of 0.99 for FTP, and 0.95 for both video streaming and low-latency traffic. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Traffic Type** & **Duration** & **Total Samples** \\ \hline FTP & \(\sim\)1 h & 14,679 \\ Video Streaming & \(\sim\)1 h & 14,287 \\ Low-Latency & \(\sim\)1 h & 14,510 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Traffic types and exact sample sizes. Figure 15: Confusion matrix of the model. The t-SNE visualization in Figure 18 portrays the distribution of predicted classes, FTP (Class 0), Video Streaming (Class 1), and Low-Latency (Class 2). Each distinct cluster in the plot corresponds to a class, revealing how the model separates and perceives these traffic types in a two-dimensional space. Overall, the plot provides a concise representation of the model's segregation of these network behaviors into distinct categories with minimal overlap. Table 9 presents a comparison of the classification accuracy achieved by various methods. This table highlights the performance of different methods, including solutions offered by other researchers, across three distinct traffic types: FTP, Video, and Low Latency. In our previous paper [4], we demonstrated high accuracy using the k-NN algorithm across all traffic types, while [PERSON] et al. 's [50] employment of CNN showed strong performance, especially in FTP and video classifications. Moreover, the utilization of Deep Packet [39] with CNN yielded consistent accuracy rates of 98% across all traffic types. [PERSON] et al. 's [51] ANN model exhibited significant challenges in accurately classifying video traffic, registering a notably lower accuracy compared to other methods. The method proposed in this paper, employing an ANN approach, showcased remarkable accuracy rates of 99.1% in FTP and 99.6% in Low-Latency traffic classification, positioning it as a competitive solution among the state-of-the-art approaches for traffic classification in network analysis. Notably, FlowPic's [40] deployment of LSTM outperformed other methods with an exceptional 99.9% accuracy in Video traffic classification, underscoring the effectiveness of recurrent neural networks in handling sequential traffic data.