# TransECA-Net: A Transformer-Based Model for Encrypted Traffic Classification
## 3 Methodology
### Cnn-ECANet  
Convolutional neural networks are extensively employed models in deep learning, particularly within the domains of computer vision and natural language processing. These networks are adept at processing images, videos, and various forms of structured data. A typical CNN architecture comprises convolutional layers, pooling layers, fully connected layers, and activation functions, all of which collaboratively facilitate the extraction of specific features from the data through convolution and pooling operations. Given the characteristics of network traffic--such as packet length, source IP address, destination IP address, source port number, destination port number, and transport layer protocol type--all of which constitute sequential data, convolutional neural networks are particularly well suited for processing such information. Their exceptional scalability  
Figure 1: TransECA-Net structural flowchart. enables CNN to effectively manage time-series data, signal data, and various forms of sequential data. Consequently, this paper adopts the CNN structure as the foundational model for the proposed TransECA-Net framework. Subsequently, we incorporate ECANet into the CNN architecture. ECANet serves as an efficient channel attention mechanism specifically designed to enhance the performance of convolutional neural networks while maintaining minimal computational overhead. By learning and weighting the significance of different channels, ECANet optimizes feature extraction. In the context of encrypted traffic analysis, it is vital to discern which features (i.e., channels) provide the richest information due to the inherent complexity and diversity of the data. ECANet autonomously identifies and prioritizes the more informative channels, thereby enhancing classification accuracy. In terms of adaptability, patterns within encrypted traffic are likely to evolve continuously, influenced by variations in encryption algorithms and protocols. ECANet equips CNNs with the capacity to adjust dynamically to these changes by modulating attention toward different channels in response to fluctuations in traffic characteristics. Below is a detailed description of the ECANet structure:  
Assume that X is the size of the input feature map, and its shape is \(\text{N}\times\text{C}\times\text{H}\times\text{W}\) (where N is the batch size, C is the number of channels, H is the height, and W is the width). For each channel C of the input characteristic diagram X, calculate the global average value of the channel. Equation (1) describes the process of calculating the global average value of the channel,  
\[z_{c}=AvgPool(X_{c})=\frac{1}{H\times\text{W}}\sum_{i=1}^{H}\sum_{j=1}^{W}X_{c }^{(i,j)} \tag{1}\]  
where is the average output of channel C, is the eigenvalue at channel C position \((i,j)\), and \(H\) and W are the height and width of the characteristic graph, respectively. Next, a 1D convolution is used to capture dependencies between channels and generate channel attention weights. Equation (2) illustrates the process of generating channel attention weights. \[\text{s}=\text{Conv}(z)=\
u^{*}\sigma(W^{*}z) \tag{2}\]  
Here, \(z\) is the channel vector obtained after global average pooling, \(W\) is the convolution weights, \(\sigma\) is the ReLU activation function, \(v\) is the output channel weight of the convolution, and the symbol \({}^{*}\) denotes the convolution operation. The output of the convolution operation is processed through a Sigmoid activation function to normalize the channel weights to the range of 0 to 1. The Sigmoid function ensures that the output weights can be used as a normalization factor. Equation (3) shows this operation. \[\beta=\sigma(s)=\frac{1}{1+e^{-s}} \tag{3}\]  
Here, \(\beta\) is the normalized channel weights, and s is the output of the convolution layer.