## 3 Results
Each ROC curve plots the true positive rate (sensitivity) against the false positive rate (1-specificity), capturing the performance of a model at various decision thresholds. The closer a curve approaches the top-left corner of the plot, the higher the true positive rate and the lower the false positive rate, which indicates superior classification performance. ROC curves are particularly valuable in security applications, as they reveal a model's ability to detect attacks under varying confidence levels and class imbalance. In all three figures, the Transformer-GAN-AE model consistently dominates, forming curves that hug the top-left boundary of the plots (resulting in a high AUC), nearly reaching 1.0. On the WUSTL-IIoT-2021 dataset (Figure 8), it sharply outpaces other models with early and sustained sensitivity, while TST and BERT form the next best group, lagging behind in the midrange thresholds. Figure 8: ROC curves of all evaluated models on the WUSTL-IIoT-2021 dataset. Figure 7: Radar chart comparison of model performance on the TON_IoT dataset. On the Edge-IIoTest dataset (Figure 9), the performance trend remains consistent, though the margins between models slightly narrow. Nevertheless, Transformer-GAN-AE still maintains the most favorable ROC shape, confirming its robustness in highly dynamic and layered environments. Similar trends are evident in Figure 10 (TON_IoT), where again the proposed model demonstrates a significantly steeper and more optimal curve compared to the baselines. These visualizations reinforce the conclusion that Transformer-GAN-AE not only achieves higher classification metrics but does so with greater stability and generalization across varying detection thresholds. The baseline models show more gradual curves (particularly GAN, CNN, and XGBoost), indicating poorer sensitivity at lower false positive rates. These insights underline the architectural advantages of combining generative augmentation, temporal attention, and feature denoising, supported by IchoA-driven optimization, all of which contribute to the proposed model's consistently superior ROC behavior. Figure 10: ROC curves of all evaluated models on the TON_IoT dataset. Figure 9: ROC curves of all evaluated models on the Edge-IIoTest dataset. Figures 11-13 present the RMSE convergence trends for all evaluated models during training on the WUSTL-IIoT-2021, Edge-IIoTset, and TON_IoT datasets, respectively. These curves visualize the model's learning behavior and optimization efficiency across 300 epochs. RMSE quantifies the difference between predicted and actual values, so a lower value indicates more accurate predictions and a more successful training phase. The faster and smoother a model's RMSE declines, the more stable and efficient its convergence process is. In all three figures, the proposed Transformer-GAN-AE demonstrates a sharp and early RMSE drop, stabilizing near zero well before the 100 th epoch and maintaining that performance throughout training. This contrasts with the baselines, especially XGBoost, CNN, and GAN, which show slower, noisier, and often incomplete convergence. Even the better-performing deep models like TST and BERT exhibit occasional fluctuations or plateaus, particularly under complex datasets such as TON_IoT. The superior convergence of Transformer-GAN-AE is a direct result of both architectural synergy and the use of the IChOA optimizer. A key contributor to this behavior is the inclusion of the Balancer chimp agent in the improved IChOA algorithm. Unlike conventional optimizer strategies that often overcommit to either exploration or exploitation, the balancer injects controlled randomness and memory-based refinement into the search process. This hybrid behavior helps avoid premature convergence and stagnation in local minima. As a result, Transformer-GAN-AE benefits from a more adaptive and stable learning trajectory.