## 2 Research Method
### Proposed the ResInceptNet-SA Model Structure
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Name** & **Branch *** & **Operation** & **Input Size** & \
\begin{tabular}{c} **Convolution** \\ **Kernel** \\ \end{tabular} & **Step** & **Padding** &
\begin{tabular}{c} **Output** \\ **Size** \\ \end{tabular} \\ \hline \multirow{5}{*}{Inception\_1} & Branch\_1 & Conv & (512,28,1) & 1 & 1 & & (512,28,32) \\  & Branch\_2 & Conv & (512,28,1) & 1 & 1 & & (512,28,48) \\  & Conv & (512,28,48) & 3 & 2 & & (512,28,64) \\  & & Conv & (512,28,1) & 1 & 1 & same & (512,28,8) \\  & Branch\_3 & Conv & (512,28,8) & 3 & 2 & & (512,28,16) \\  & & Conv & (512,28,16) & 3 & 2 & & (512,28,16) \\  & Branch\_4 & Max Pool & (512,28,1) & 1 & 0 & & (512,28,1) \\  & & Conv & (512,28,1) & 1 & 2 & & (512,28,16) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Parameters of the Inception_1 unit. Figure 4: Inception_1. Figure 3: Residual block. uniform scale, which enhanced its ability to learn robust and generalized features. The structure of this unit is shown in Figure 5.  
The * symbol indicates that the column is arranged in ascending order, corresponding to the branch order from left to right in Figure 5.  
The Inception_3 unit consisted of two branches: the first performed sampling and dimensionality reduction, while the second focused on both sampling and dimensionality reduction, with additional data preservation mechanisms. This dual-branch approach aimed to balance the trade-off between reducing computational complexity and retaining important data features, which improved the model's capacity to extract meaningful patterns from the input. The structure and function of this unit are illustrated in Figure 6, and the corresponding parameters are provided in Table 3. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Name** & **Branch * & **Operation** & **Input Size** & \begin{tabular}{c} **Convolution** \\ **Kernel** \\ \end{tabular} & **Step** & **Padding** & **Output Size** \\ \hline Inception\_2 & \begin{tabular}{c} Branch\_1 \\ Branch\_2 \\ \end{tabular} & \begin{tabular}{c} Conv \\ Conv \\ \end{tabular} & \begin{tabular}{c} (512,28,128) \\ (512,28,128) \\ \end{tabular} & \begin{tabular}{c} 3 \\ 3 \\ 3 \\ \end{tabular} & \begin{tabular}{c} 1 \\ 1 \\ \end{tabular} & \
\begin{tabular}{c} valid \\ \end{tabular} &
\begin{tabular}{c} (512,26,64) \\ (512,26,64) \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Parameters of the Inception_2 unit. Figure 5: Inception_2. Figure 6: Inception_3. The SA module was positioned at the end of the model and was responsible for computing the attention values from the feature vector output by the InceptionNet module, which were then passed to the detection layer. This process enhanced the model's expressiveness and adaptability, improving its classification performance. The SA mechanism calculates the attention score based on the query (\(Q\)), key (\(K\)), and value (\(V\)), where \(X\) is the input and \(W\) is the learning weight. \(Q\), \(K\), and \(V\) are computed using Equation (3), Equation (4), and Equation (5), respectively. The attention score (\(A_{S}\)) is then computed using Equation (6), where \(d_{q}\) is the length of \(q\). Finally, the attention value (\(A_{V}\)) is computed using the attention score and the value \(V\), as shown in Equation (7). \[Q=W_{q}\cdot X \tag{3}\]  
\[K=W_{k}\cdot X \tag{4}\]  
\[V=W_{p}\cdot X \tag{5}\]  
\[A_{S}=\frac{Q\cdot K^{T}}{\sqrt{d_{q}}} \tag{6}\]  
\[A_{V}=softmax(A_{S})\cdot V \tag{7}\]  
A detection layer was added at the end of the proposed model. This layer consisted of a global average pooling layer, a fully connected layer, and a softmax activation function, used for classifying the traffic data processed by the model. In the detection module, the first fully connected layer was replaced by a global average pooling layer. This modification reduced the number of parameters, mitigated overfitting, and improved both training efficiency and classification performance.