## 3 Results
CNN's architecture was fixed at 10 convolutional layers with 5 \(\times\) 5 kernels and 64 neurons, leveraging max pooling for local spatial abstraction. DBN used six hidden layers with 32 neurons each and tanh-sigmoid activation under SGD, while XGboost achieved optimal results using a learning rate of 0.29, depth of 6, and 300 estimators, highlighting its fast convergence and ensemble strength. These tuned configurations allowed for a fair and competitive benchmarking environment against the optimized Transformer-GAN-AE model. In Table 2, the performance of the proposed Transformer-GAN-AE model is compared against six well-established baseline algorithms, including BERT, TST, DBN, GAN, CNN, and XGBoost. The table presents three key classification metrics (accuracy, recall, and AUC) evaluated across all three datasets. This comparative analysis captures not only the general correctness of each model (accuracy) but also its ability to detect attack instances (recall) and its overall discriminative power across decision thresholds (AUC). Together, these metrics offer a robust and comprehensive understanding of each model's detection performance and generalization ability in IIoT security settings. The proposed Transformer-GAN-AE consistently outperforms all baseline models across all metrics and datasets. On the WUSTL-IIoT-2021 dataset, it achieves an accuracy of 97.86%, a recall of 98.63%, and an AUC of 99.18%, surpassing the next best model (BERT) by more than 6.5% in accuracy and nearly 6 points in AUC. A similar pattern is observed in Edge-IIoTset, where Transformer-GAN-AE achieves the highest scores in all three metrics: 98.63% accuracy, 98.79% recall, and 99.53% AUC. On the most challenging and diverse dataset, TON_IoT, the model again outperforms the alternatives with 98.92% accuracy, 99.52% recall, and a near-perfect AUC of 99.87%. These results clearly demonstrate the superiority of the proposed model in both balanced and imbalanced settings. This performance advantage can be attributed to several architectural strengths. The transformer encoder effectively captures temporal dependencies and contextual patterns in time-series intrusion data, while the GAN component synthesizes realistic minority class samples, reducing the impact of data imbalance. The AE, placed downstream, enhances anomaly sensitivity through reconstruction-based filtering. The integration of these three components allows the model to learn both generative and discriminative representations, which leads to stronger decision boundaries and higher recall in detecting subtle or rare attack patterns. In contrast, single-module baselines such as CNN or DBN lack the composite learning capacity needed for multi-layered intrusion behaviors. Furthermore, the use of the IChOA for hyperparameter tuning has contributed significantly to the model's stability and generalization. Unlike the grid search used for baseline models, IChOA adaptively explores the search space and fine-tunes key parameters such as attention head count, latent dimension size, and learning rate. This optimization ensures that the model converges rapidly and remains robust across datasets. The results confirm that Transformer-GAN-AE