## 3 Results
\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 \\ BERT & 91.36 & 92.08 & 93.47 & 90.24 & 91.20 & 92.54 & 92.38 & 93.61 & 94.51 \\ TST & 89.29 & 90.34 & 91.48 & 90.35 & 91.96 & 93.07 & 90.18 & 91.78 & 92.36 \\ DBN & 87.35 & 88.49 & 89.20 & 88.26 & 89.29 & 90.28 & 91.27 & 92.69 & 93.79 \\ GAN & 85.64 & 85.69 & 87.18 & 86.31 & 87.26 & 88.96 & 89.16 & 90.65 & 90.08 \\ CNN & 83.19 & 84.17 & 85.68 & 85.41 & 86.92 & 88.47 & 86.31 & 87.09 & 89.14 \\ XGBoost & 80.18 & 81.24 & 82.74 & 81.60 & 82.76 & 83.19 & 81.64 & 83.61 & 83.17 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparative performance of the proposed model and other methods across three datasets. is not only more accurate but also more resilient to data variability, making it an ideal solution for deployment in real-world IIoT and Edge-based intrusion detection scenarios. To further investigate the effectiveness of the proposed IChOA optimizer beyond our main framework, Table 3 presents the performance of multiple baseline architectures (namely BERT, TST, DBN, GAN, and CNN) when optimized using IChOA rather than traditional tuning methods. The goal is to isolate the impact of IChOA across different models and understand whether the observed improvements in our model are purely due to optimization or stem from deeper architectural synergy. The results in Table 3 demonstrate that applying IChOA led to consistent performance improvements across nearly all baseline models compared to their original versions in Table 2. For example, BERT's accuracy improved from 91.36% to 92.03% on WUSTL-IIoT-2021 and from 92.38% to 93.96% on TON_IoT. Similarly, TST showed significant gains, reaching 92.29% accuracy on TON_IoT, compared to 89.29% previously. These improvements confirm that IChOA is an effective optimizer for deep and hybrid models in complex, high-dimensional settings such as IIoT intrusion detection. Despite these enhancements, none of the IChOA-augmented baseline models surpassed the proposed Transformer-GAN-AE architecture, which still maintained the highest accuracy, recall, and AUC across all three datasets. This suggests that while optimization plays a critical role in improving model performance, the synergistic interaction between the Transformer, GAN, and AE modules in our architecture provides a unique advantage, particularly in capturing temporal dependencies, balancing class distributions, and denoising feature representations. Additionally, Table 3 includes two architectural variants (Transformer-CNN-AE and Transformer-DBN-AE) that replace the GAN component with CNN and DBN, respectively. Although both models achieved relatively high scores, they consistently lagged behind the full Transformer-GAN-AE configuration. This highlights the importance of the GAN's generative capacity in enhancing minority class representation and further confirms the necessity of all three components working in tandem for optimal detection performance. Figure 6 presents a graphical summary of the model performance metrics shown in Table 2, highlighting differences across architecture types concerning accuracy, recall, and AUC. Subfigure (a) corresponds to results on the WUSTL-IIoT-2021 dataset, while subfigure (b) depicts the same metrics on Edge-IIoTset. Each cluster of bars compares the performance of the proposed Transformer-GAN-AE with six baseline models: BERT, TST, DBN, GAN, CNN, and XGBoost. The visual format accentuates performance margins, making it easier to observe relative advantages and stability across evaluation criteria.