## 5 Material and Methods
### Attack Detection Module
Figure 5: The overall architecture of the 1 DCNN-DT hybrid artificial intelligence model. These deep features are then used to train a Decision Tree (DT) classifier. In the final phase of the model, the accuracy of the proposed system is evaluated using the test dataset. The following sections provide a detailed explanation of each phase of the proposed model. In the 1 DCNN-DT hybrid AI model, three convolutional layers are utilized. The convolutional layers filter the data to extract deep features, and the weights of these filters are optimized during the model's training process [42; 43]. An example of the convolution process is presented in Figure 6.  
Here, \(w\) represents the filter weights (the parameters to be trained), \(x\) denotes the input, \(w_{0}\) is the bias, and \(N_{output}\) represents the convolution output. In Figure 6, the matrix \(x\) on the left represents the input data, where each cell corresponds to a pixel or data point derived from the input. The matrix \(w\) at the top shows the filter (kernel) used in the convolutional layer. This filter functions like a sliding window over the data, applied at multiple positions across the input to help extract specific features [44]. The convolution operation is performed by multiplying the weights in the filter with the corresponding sections of the input data, followed by adding the bias value \(w_{0}\). This process is repeated at each position to generate a new output matrix, \(N_{output}\). The data presented as input to the 1 DCNN-DT hybrid artificial intelligence model \((X_{1},X_{2},\dots X_{N})\), are first fed into a 1-dimensional convolution (1D Conv) layer with a kernel size of 5 and 16 filters. This initial convolutional layer facilitates the extraction of basic patterns from the input data. Following this layer, non-linear features are defined through the ReLU (Rectified Linear Unit) activation function, and the data are passed to the second 1D convolutional layer. In the second convolutional layer, 32 filters with a kernel size of 5 are used to extract deeper and more complex features. The third convolutional layer employs 64 filters with a kernel size of 5 [45]. Each convolutional layer extracts features at different levels from the input data. The resulting feature maps are then transformed into a one-dimensional vector via a flattened layer. In the final stage, these deep features are passed to a decision tree classifier model. The decision tree classifier assigns the data samples to a specific class based on the extracted deep features [46]. The development of the 1 DCNN-DT hybrid artificial intelligence model consists of three main phases. In the first phase, a CNN architecture, primarily composed of three convolutional layers and two fully connected layers, is constructed. The CNN is trained using the training dataset, and as a result, deep features are extracted. In the second phase, these deep features are fed into a Decision Tree classifier, where the samples are classified. In the final phase, the performance and accuracy of the proposed model are evaluated using the test dataset. Figure 6: The convolution process.