## 5 Conclusions  
Intrusion detection in IIoT and edge environments remains a critical challenge due to the complexity, heterogeneity, and high dimensionality of networked systems. Traditional models often struggle to adapt to evolving threat patterns, class imbalance, and temporal dependencies present in real-world data. In this paper, we proposed a novel hybrid DL framework (Transformer-GAN-AE) designed to address these challenges through a synergistic combination of generative learning, sequence modeling, and reconstruction-based \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Algorithm**} & \multicolumn{3}{c}{**Variance**} \\ \cline{2-4}  & **WUSTL-IIoT-2021** & **Edge-IIoTset** & **TON\_IoT** \\ \hline Transformer-GAN-AE & 0.00009 & 0.00011 & 0.00008 \\ BERT & 1.98652 & 2.31425 & 1.59632 \\ TST & 2.02413 & 3.54789 & 1.90258 \\ DBN & 2.14756 & 3.96321 & 3.41256 \\ GAN & 3.25058 & 4.09652 & 3.08652 \\ CNN & 5.21478 & 6.20035 & 4.98563 \\ XGboost & 8.32526 & 8.18963 & 7.52147 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Variance of model performance across multiple independent runs. anomaly detection. The architecture integrates a transformer encoder to capture long-range temporal dependencies, a GAN to synthesize realistic minority class samples, and an AE to refine features and suppress noise in the data. To enhance model generalization and training stability, we incorporated an IChOA featuring a novel balancer agent that adaptively regulates the trade-off between exploration and exploitation during hyperparameter tuning. The proposed model was evaluated on three publicly available benchmark datasets, WUSTL-IIoT-2021, Edge-IIoTset, and TON_IoT, each representing diverse IIoT security scenarios. Extensive experiments, including comparisons with state-of-the-art models such as CNN, DBN, TST, BERT, and XGBoost, demonstrated that Transformer-GAN-AE consistently outperforms all baselines in terms of accuracy, recall, AUC, convergence speed, runtime efficiency, and statistical robustness. On the WUSTL-IIoT-2021 dataset, the proposed model achieved an accuracy of 97.86%, a recall of 98.63%, and an AUC of 99.18%, surpassing the next-best model (BERT) by 6.5%, 5.7%, and 4.8%, respectively. On the more complex TON_IoT dataset, Transformer-GAN-AE achieved 98.92% accuracy, 99.52% recall, and an almost perfect AUC of 99.87%, outperforming all alternatives across all metrics. Furthermore, the radar and ROC plots illustrated consistent dominance across performance dimensions, confirming the model's superior generalization and anomaly sensitivity. Beyond classification performance, the proposed model also exhibited superior training dynamics and operational stability. It reached an RMSE below 2.5 in just 128 s, while all other models either failed to converge or required significantly more time. A \(t\)-test analysis showed that improvements over baselines were statistically significant (\(p<0.01\) for all comparisons), while variance analysis revealed minimal fluctuation across repeated runs, less than 0.0001 on all datasets, highlighting Transformer-GAN-AE's robustness and consistency. These results collectively validate the effectiveness of the proposed architecture and its IChOA-based optimization in delivering a scalable, accurate, and dependable solution for real-world IIoT intrusion detection. The findings of this study underscore a fundamental insight: addressing complex cyber threats in edge and IIoT environments requires not only higher accuracy but also architectural adaptability, robust optimization, and systemic consistency. The superior performance of Transformer-GAN-AE is not solely due to its composite structure, but rather to the cohesive interaction between its components, each of which fulfills a specific, complementary function. The transformer encoder captures evolving patterns over time, the GAN enhances data diversity and combats imbalance, and the AE filters anomalies with sensitivity to reconstruction variance.