## 3 Methodology
### Artificial Neural Network  
The objective of incorporating an MLP in our framework is to take advantage of its powerful pattern recognition capabilities [45] to accurately classify low-latency Internet traffic. In our approach, the MLP serves as the core component of the artificial neural  
Figure 10: Slope of throughput for downlink and uplink traffic. network model, which is essential to analyze the complex temporal dynamics present in the network traffic data. Using MLP, our aim is to effectively distinguish between various types of Internet traffic, ensuring high accuracy in identifying low-latency traffic patterns. The multilayer perceptron belongs to a category of feed-forward artificial neural networks as depicted in Figure 11. It typically comprises three or more layers. The initial layer is designated for receiving input data. Subsequent hidden layers, one or more in number, are responsible for extracting relevant features from the input data. The final layer produces a classification result. Each hidden layer, such as the \(i-th\) layer, is constructed with multiple neurons primarily utilizing a nonlinear activation function as described in Equation (7) below:  
\[f(x)=\sigma(W^{(i)}\cdot x+b^{(i)}), \tag{7}\]  
where \(\sigma(\cdot)\) denotes an activation function, for example, \(\sigma(x)=tanh(x)\). A key attribute of the activation function is its ability to provide a smooth transition as input values change. \(W^{(i)}\) represents a weight matrix, and \(b^{(i)}\) is a bias vector. It is possible to have multiple hidden layers, and each layer performs the same function but with distinct weight matrices and bias vectors. The final layer produces the output based on the results of the last hidden layer, often denoted as layer \(j\) and defined in Equation (8):  
\[\sigma(x)=g(W^{(j)}\cdot x+b^{(j)}), \tag{8}\]  
The deep learning model employed in this study is a feedforward neural network with three layers demonstrated in Figure 11: an input layer, two hidden layers, and an output layer. The architecture is mathematically defined as follows. Equation (9) represents the model's input vector, composed of all feature types. The operations in Equations (10)-(15) define the transformations in the first and second hidden layers and the final softmax output layer. Figure 11: Overview of the MLP-based ANN classifier. * Input Layer: The input layer consists of \(N\) neurons, where \(N\) represents the number of features in the dataset. These features include throughput \(T\), moving averages \(MA\), downlink-to-uplink ratio \(R\), slope \(S\) and wavelet transformed counterparts of them \(WT\). The input vector \(X\) is represented as \[X_{input}=[T_{i},MA_{i},R_{i},S_{i},WT_{i}],\] (9) where \(X_{i}\) represents the value of the feature \(i\). * First Hidden Layer: The first hidden layer has \(H_{1}\) neurons, where \(H_{1}=8\) in this implementation. Each neuron applies the Rectified Linear Unit (ReLU) activation function \(f_{\text{ReLU}}(x)=\max(0,x)\) to its weighted sum of inputs. Mathematically, for each neuron \(j\) in the first hidden layer, \[Z_{j}^{(1)}=\sum_{i=1}^{N}W_{ij}^{(1)}\cdot X_{i}+b_{j}^{(1)},\] (10) \[A_{j}^{(1)}=f_{\text{ReLU}}(Z_{j}^{(1)}),\] (11) where \(W_{ij}^{1}\) represents the weight of the connection between \(b_{j}^{(1)}\) is the bias term for neuron \(j\). * Second Hidden Layer: The second hidden layer consists of \(H_{2}\) neurons, where \(H_{2}=4\) in this configuration. Similar to the first hidden layer, each neuron applies the ReLU activation function to its weighted sum of inputs. Mathematically, for each neuron \(k\) in the first hidden layer, \[Z_{k}^{(2)}=\sum_{j=1}^{H_{1}}W_{jk}^{(2)}\cdot A_{j}^{(1)}+b_{k}^{(2)},\] (12) Figure 11.0 (13) where \(W_{ij}^{1}\) represents the weight of the connection between \(b_{j}^{(1)}\) is the bias term for neuron \(j\). * Output Layer: The output layer comprises \(C\) neurons, where \(C\) is the different number of traffic classes in the dataset. It uses the softmax activation function to produce class probabilities.