## 3 Results
As shown, the Transformer-GAN-AE significantly outperforms all competing models across all metrics and datasets, maintaining values near or above 98% in both accuracy and recall, with AUC scores approaching 100%. These consistently high bars reflect the model's ability \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{3}{c}{**WUSTL-IIoT-2021**} & \multicolumn{3}{c}{**Edge-IIoTset**} & \multicolumn{3}{c}{**TON\_IoT**} \\ \cline{2-10}  & **Accuracy** & **Recall** & **AUC** & **Accuracy** & **Recall** & **AUC** & **Accuracy** & **Recall** & **AUC** \\ \hline Transformer-GAN-AE & 97.86 & 98.63 & 99.18 & 98.63 & 98.79 & 99.53 & 98.92 & 99.52 & 99.87 \\ Transformer-CNN-AE & 92.34 & 93.65 & 93.89 & 93.25 & 93.98 & 94.24 & 93.08 & 93.59 & 94.29 \\ Transformer-DBN-AE & 92.89 & 94.05 & 94.21 & 93.89 & 94.32 & 94.09 & 94.01 & 94.82 & 95.67 \\ BERT-IChOA & 92.03 & 93.20 & 94.12 & 91.25 & 92.34 & 93.32 & 93.96 & 94.42 & 95.41 \\ TST-IChOA & 90.48 & 91.18 & 92.27 & 91.76 & 92.70 & 93.15 & 92.29 & 93.93 & 94.89 \\ DBN-IChOA & 88.46 & 89.40 & 90.33 & 89.43 & 90.12 & 91.20 & 92.76 & 93.87 & 94.40 \\ GAN-IChOA & 86.41 & 86.77 & 88.34 & 87.16 & 88.81 & 89.32 & 90.27 & 91.50 & 91.28 \\ CNN-IChOA & 84.25 & 85.32 & 86.46 & 86.40 & 87.70 & 89.76 & 87.63 & 88.28 & 90.36 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparative performance of all models after IChOA-based hyperparameter optimization. to balance precision and recall, while also achieving superior discriminatory power across thresholds. In contrast, BERT and TST form a second tier of performance, with noticeably lower AUC and recall values. The models based on CNN, DBN, and XGBoost occupy the lowest tier, particularly in AUC, which highlights their limited capacity to differentiate attack from benign behavior under varied conditions. The visual clarity offered by Figure 6 reinforces the superiority of the proposed architecture and its ability to maintain robustness across complex IIoT datasets. Figure 7 illustrates a radar plot that visually compares the performance of the proposed Transformer-GAN-AE architecture against six baseline models. In radar charts, each axis represents a separate metric, and the plotted area corresponds to a model's magnitude in that metric. Larger, outward-spreading shapes indicate stronger overall performance. This visualization is particularly effective for multi-criteria comparisons as it simultaneously captures the balance and dominance of each model across multiple axes. From the chart, it is evident that Transformer-GAN-AE forms the largest and most balanced polygon, nearly saturating all three axes with values above 98%, indicating that it dominates consistently in terms of detection correctness, sensitivity, and discriminative power. In contrast, models such as XGBoost and CNN form much smaller and more asymmetric regions, suggesting weaker and less uniform performance across metrics. BERT and TST perform relatively well, but still fall short of the proposed model in terms of AUC and recall, especially on the high-dimensional, imbalanced TON_IoT dataset. The radar plot confirms the robustness of Transformer-GAN-AE under complex IIoT conditions, where maintaining high values across all evaluation metrics is critical. Models with irregular or concave polygons are more prone to trade-offs, performing well on one metric while underperforming on others. The ability of the proposed model to form a near-perfect, outward-reaching triangle suggests its superior optimization, balanced architecture, and improved generalization. This reinforces the numerical findings from Table 2 and supports the model's suitability for real-world deployment in mission-critical environments. Figure 6: Visual comparison of detection performance across models: (a) WUSTL-IIoT-2021; (b) Edge-IIoTset. Figures 8-10 present the ROC curves for all models across the three benchmark datasets.