## 5 Experimental Results and Analysis  
The experiments were meticulously planned to assess the efficacy of our work by contrasting it with state-of-the-art classification methods. To ensure that our evaluation is appropriate for the problem of low-latency Internet traffic classification, we designed our experiments with the following considerations: \begin{table}
\begin{tabular}{c c c} \hline \hline
**Hyperparameters** & **Range** & **Selection** \\ \hline Number of hidden layers & [1, 2, 3, 4, 5] & 2 \\ Activation Function & [sigmoid, tanh, ReLU] & ReLU \\ Learning Rate & [0.1, 0.01, 0.001] & 0.001 \\ Batch Size & [16, 32, 64, 128] & 32 \\ Number of Epochs & [10, \(\dots,50,\)\(\dots,\) 150] & 100 \\ Optimizer & [Adam] & Adam \\ Dropout Rate & [0, 0.1, 0.2, 0.3, 0.4, 0.5] & 0 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Hyperparameter tuning for ANN model. * Diverse Traffic Types: We included a variety of traffic types such as FTP, video streaming, and low-latency traffic. This selection ensures that our model is tested against different patterns of Internet traffic, reflecting real-world scenarios. * Balanced Dataset: The dataset used for training and testing the classification algorithm was designed to maintain a balanced representation of each traffic type, with approximately one hour's worth of sampling per category. This balance is critical for avoiding bias in the model's performance. * Comparison with Established Methods: Our approach was compared with state-of-the-art classification methods, including k-NN, CNN, and LSTM-based models, as highlighted in various studies [4; 39; 40; 50; 51]. This comparison not only validates the robustness of our model but also situates our results within the context of existing research. * Evaluation Metrics: We measured the model's performance using key metrics such as accuracy, precision, recall, and the F1 score. These metrics are standard in the field of traffic classification and provide a comprehensive assessment of the model's effectiveness. * Confusion Matrix Analysis: The use of confusion matrices allowed us to visualize the classification performance across different traffic types, providing insights into the strengths and weaknesses of our model. * Mixed Traffic Scenarios: We evaluated our model under both simple and complex traffic scenarios to understand its performance in real-world conditions where multiple types of traffic coexist. This evaluation is crucial for demonstrating the practical applicability of our approach. Our initial experiments focused on evaluating the classification performance in mixed traffic scenarios involving different types of Internet traffic, such as File Transfer Protocol (FTP), video streaming, and low-latency traffic. These scenarios were selected based on a section of the [47] and our own experiences. These scenarios reflect real-world conditions where multiple types of traffic coexist, providing a comprehensive test of the model's ability to accurately identify the traffic types. We designed a series of basic traffic scenarios to assess the model's accuracy. As shown in Table 5, these scenarios consisted of different combinations of two or more traffic types, with special attention to the presence of low-latency traffic. The results demonstrated that the model achieved higher accuracy when low-latency traffic was involved, with the highest accuracy of 96.5% in the scenario where both instances were low-latency traffic. In scenarios without low-latency traffic, the accuracy was slightly lower, such as 88.3% in the case of two FTP instances. In addition to the basic traffic scenarios, we designed a set of complex traffic scenarios to further evaluate the model's performance in more challenging environments where three or more types of traffic coexist. These scenarios provide insight into how the model