# TransECA-Net: A Transformer-Based Model for Encrypted Traffic Classification
## 3 Methodology
### Cnn-ECANet
Finally, each channel of the input feature map is multiplied by its corresponding attention weight to enhance or suppress the features of specific channels, as shown in the following Equation (4):  
\[Y_{c}=\beta_{c}\cdot X_{c} \tag{4}\]  
where \(Y_{c}\) is the weighted output channel, \(\beta_{c}\) is the channel attention weight, and \(X_{c}\) is the original input channel. The flowchart of the ECANet structure is shown in Figure 2.  
In the TransECA-Net model, the operational flow of the CNN layer encompasses several systematic steps:  
1. 2D convolutional layer: Initially, a 2D convolutional layer processes the input feature map, configured with 1 input channel and 32 output channels. The layer uses a kernel size of (1, 25), a stride of 1, and \"same\" padding to ensure that the output dimensions are consistent with those of the input. 2. Activation function layer: Following the convolutional layer, an activation function layer applies the ReLU function. This introduction of non-linearity is crucial for enhancing the model's capacity to learn complex patterns. 3. ECAModule: Next, an ECAModule is implemented. This effective channel attention module adaptively modifies the weights of various channels through the application of 1D convolution followed by a Sigmoid activation function. This mechanism enables the model to focus on the most important features in the data. 4. Max pooling layer: Subsequently, a max pooling layer is employed to reduce the spatial dimensions of the feature maps. This layer is configured with a kernel size of (1, 3) and a stride of 3, leading to a more compact representation of the features while retaining the essential information. 5. Repetition: The previously mentioned configurations are repeated for a second convolutional layer, where the number of output channels is increased to 64. This enhancement allows the model to capture more intricate features and patterns in the input data. Through this structured approach, the CNN layer is able to effectively extract and refine pertinent features from the input, contributing to the overall robustness of the TransECA-Net model. In the domain of classifying encrypted network traffic, the incorporation of ECANet into the CNN architecture yields several distinct advantages. Unlike the straightforward one-dimensional convolutional neural network structure utilized by [PERSON] [20], this paper enhances the CNN framework by integrating the ECANet module. This attention mechanism effectively boosts the network's performance while maintaining minimal computational overhead, particularly by emphasizing the significance of inter-channel relationships, thereby improving feature extraction and learning efficiency. In comparison to the SENet employed in [PERSON] et al. 's research [23], ECANet presents a more efficient alternative that retains the beneficial aspects of channel attention while achieving superior computational efficiency and adaptability. This efficiency makes ECANet an appealing choice for addressing large-scale or complex image-recognition tasks, particularly in contexts where both efficiency and performance are critical considerations. Overall, the integration of ECANet within the CNN framework significantly enhances the model's ability to classify encrypted network traffic effectively.