## 5 Experiments and Analysis of Results
### Performance of DOA-BPNN Model  
To evaluate the effectiveness of the proposed model in intrusion detection tasks, experiments were conducted under both multi-class and binary classification settings using consistent parameter configurations. As shown in Figure 5, the DOA optimization process shows a steadily increasing trend in both best fitness and average fitness, with best validation accuracy exceeding 90% after 37 iterations and ultimately reaching 93.47% by iteration 60. This demonstrates the optimizers's ability to progressively improve parameter quality over time. Figure 5 compares the training processes of the BP neural network with and without DOA-optimized initialization. The DOA-BP model achieves faster convergence and a lower final loss value compared to the standard BP network, highlighting that incorporating DOA optimization into the BP framework enhances overall training effectiveness and detection performance. As shown in Figure 6, the proposed DOA-BP model outperforms the conventional BP neural network across almost all attack classes in the multi-class detection task. It achieves higher accuracy, precision, recall, and F1-scores in most classes, with only a slight F1-score decrease observed in the Backdoor class, from 95.97% to 94.09%, despite maintaining the same high accuracy of 99.95%. Notably, in challenging classes such as Normal and Password, DOA-BP delivers significant improvements. For Normal, accuracy and recall increase from 66.12% to 85.01%, and F1-score rises from 78.03% to 90.43%, indicating that when DOA-BPNN is applied to intrusion detection in T2G communication systems, it can more accurately recognize normal communication behaviors, thereby reducing the impact of false alarms that could trigger unnecessary safety measures. For Password, accuracy improves from 87.39% to 96.25%, and F1-score from 85.82% to 94.67%, showing the model's effectiveness in detecting attacks targeting access control vulnerabilities in T2G  
Figure 5: Performance of DOA-BPNN training process: (**a**) accuracy evolution during DOA-based parameter optimization; (**b**) loss convergence trend in the training of BP neural network initialized with DOA-optimized weights. communication systems, which are a critical security concern. In the Ransomware class, DOA-BP also shows strong gains, with accuracy increasing from 80.69% to 96.82% and F1-score from 84.04% to 91.80%, indicating a notable improvement in detecting data integrity threats that are crucial in railway operations. Despite these improvements, the Normal class remains the weakest point for both models, as its accuracy and recall, while improved, are still the lowest among all classes. This suggests that distinguishing benign traffic from actual threats remains challenging and warrants further investigation. Overall, these results highlight the consistent performance enhancements achieved by DOA-BP, while also pointing to key areas for future optimization, such as further refining detection for benign traffic and attacks targeting access control vulnerabilities in T2G communication systems. To further assess the effectiveness of the proposed DOA-BP model, it was compared against BP neural networks optimized by four commonly used metaheuristic algorithms: WOA [37], GWO [38], SCA [39], and PSO [40]. Figure 7 shows that all optimization methods improve upon the baseline BP network, with GWO-BP and DOA-BP exhibiting the most significant performance gains. Specifically, DOA-BP achieves an accuracy of 95.78% and an F1-score of 95.77%, slightly outperforming GWO-BP, which reaches 95.63% accuracy and 95.61% F1, by a small but consistent margin. These results confirm the effectiveness of applying DOA for optimizing BPNN parameters, demonstrating that even marginal improvements can be crucial when fine-tuning detection models for sensitive tasks such as T2G communication systems.