## 4 Results and Discussion
### Comparison with Existing Models  
In this experiment, the NSL-KDD dataset processed by the PCA-ADASYN algorithm was used to compare the performance of seven deep learning models during training, including AlexNet, LenNet, DCNN, LSTM, BiLSTM, CNN-BiLSTM, and ResIncepNet-SA, as shown in Figure 11.  
Figure 10: Comparison of the model’s F1-Score and testing time for the four datasets.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Method** & **Accuracy** & **Precision** & **Recall** & **F1-Score** & **Testing Time(s)** \\ \hline PCA-ADASYN & 0.99339 & 0.99343 & 0.99339 & 0.99338 & 31.6 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Model results using PCA-ADASYN indicators.  
The accuracy of ResIncepNet-SA exceeded 0.96 in the third epoch and reached its peak in the 50 th epoch. During training, the fluctuation of the proposed model's accuracy gradually decreased. This was because an adaptive learning rate was used during model training. When the accuracy no longer improved every 5 epochs, the learning rate was halved to promote faster convergence of the model.  
To verify the comprehensive performance of the model, a further comparison was made between the seven models in the classification task. The results show that the proposed model presented in this paper outperformed the others in terms of accuracy, precision, recall, and F1-score, as shown in Figure 12.  
Figure 11: Comparison of the training process of various anomalous traffic detection algorithms.  
Figure 12: Experimental comparison of various anomalous traffic detection algorithms.  
Based on the experimental results of the proposed model, a confusion matrix was generated to evaluate the classification errors. The diagonal elements represented the accuracy of correct predictions, while the off-diagonal elements indicated the percentage of incorrect predictions, as shown in Figure 13. The confusion matrix demonstrated that the proposed model presented in this paper was capable of accurately identifying different types of abnormal traffic.  
This paper compared the proposed ResIncepNet-SA model with existing intrusion detection methods, all of which used the NSL-KDD dataset, and evaluated the model using accuracy, precision, recall, and F1-Score metrics. Existing methods typically optimize neural networks through various techniques to enhance the performance of traditional models. In contrast, the proposed model presented in this paper achieved higher accuracy by utilizing the NSL-KDD dataset processed with PCA-ADASYN. The comparison results are shown in Table 10.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Doc** & **Year** & **Method** & **Accuracy** & **Precision** & **Recall** & **F1-Score** \\ \hline
[46] & 2022 & PSO-GWO & 0.942 & 0.96 & 0.86 & 0.907 \\
[47] & 2023 & Transformer & 0.841 & - & - & 0.838 \\
[48] & 2024 & LR-ABC & 0.9011 & - & - & 0.9015 \\
[49] & 2023 & ANN & 0.975 & 0.99 & 0.967 & 0.957 \\
[50] & 2023 & Sparse AE & 0.9516 & 0.9421 & - & 0.967 \\ Proposed model & - & ResIncepNet-SA & 0.99339 & 0.99343 & 0.99339 & 0.99338 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Performance comparison of the proposed model with existing methods.  
Figure 13: ResInceptNet-SA model confusion matrix.