## 3 Results
Finally, the SVM model utilized an RBF kernel, with a gamma value of 0.004,300 estimators, and a standard regularization parameter of 1, offering a strong baseline for lower-dimensional binary and multi-class classification tasks. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Proposed Methods** & **Parameters** & **Value** \\ \hline \multirow{8}{*}{Transformer-GAN} & Learning rate & 0.001 \\  & Batch size & 32 \\  & Feed forward hidden size & 2048 \\  & Weight decay & 0.01 \\  & Dropout rate & 0.1 \\  & Number of attention heads & 10 \\  & Number of decoder layers & 6 \\  & Number of encoder layers & 6 \\  & Activation function & GELU \\  & Optimizer & INSBBO \\  & Momentum term & 0.04 \\  & Convergence threshold & 0.066 \\  & Probability range for migration & [0, 1] \\  & Mutation rate & 0.06 \\  & Population Size & 100 \\  & Iteration & 300 \\ \hline \multirow{8}{*}{Transformer} & Learning rate & 0.002 \\  & Batch size & 64 \\  & Feed forward hidden size & 2048 \\  & Weight decay & 0.01 \\  & Dropout rate & 0.1 \\  & Number of attention heads & 12 \\  & Number of decoder layers & 8 \\  & Number of encoder layers & 8 \\  & Activation function & GELU \\  & Optimizer & Adam \\ \hline \multirow{8}{*}{LSTM} & Learning rate & 0.004 \\  & Batch size & 64 \\  & Recurrent dropout rate & 0.3 \\  & Sequence length & 6 \\  & Number of hidden layers & 8 \\  & Number of neurons per layer & 64 \\  & Activation function & Tanh and sigmoid \\  & Optimizer & SGD \\ \hline \multirow{8}{*}{CNN} & Number of convolution layers & 6 \\  & Kernel size & 3 \(\times\) 3 \\  & Pooling type & Max pooling (2 \(\times\) 2) \\  & Number of neurons & 28 \\ \hline \multirow{4}{*}{SVM} & Regularization parameter & 1 \\  & Number of estimators & 300 \\ \cline{1-1}  & Gamma & 0.004 \\ \cline{1-1}  & Kernel type & RBF \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Parameter setting of proposed models. Table 4 demonstrates comparative performance of all models in binary and multi-class intrusion detection in the CIC-IoT-2023 dataset. The proposed transformer-GAN model achieves the highest performance across all evaluation metrics in both binary and multi-class classification tasks. In binary classification, it reaches an impressive accuracy of 99.76%, F1-score of 99.63%, and AUC of 99.91%, substantially outperforming all other baselines. In the more challenging multi-class classification setting, it maintains its superiority with an accuracy of 99.53%, F1-score of 99.42%, and AUC of 99.71%. To further isolate the contribution of the INSBBO optimizer, we evaluated a version of the architecture that combines only transformer and GAN, without any optimization. This configuration achieved strong results, with 96.75% accuracy in binary and 94.89% in multi-class classification, ranking second across all evaluation metrics. The standalone transformer model ranks third, with solid results (e.g., 95.28% accuracy in binary and 94.49% in multi-class), reflecting its strong capacity for sequence modeling. However, the addition of adversarial training in the hybrid transformer-GAN evidently enhances its feature representation and decision boundaries. GAN alone, although competitive, falls behind the hybrid due to weaker temporal modeling and lack of direct classification tuning. LSTM, while effective for sequence data, demonstrates lower generalization in this context, with accuracies of 90.48% and 89.60% in binary and multi-class modes, respectively. CNN and SVM exhibit relatively lower performance, likely due to their limited ability to capture long-range temporal and contextual dependencies in IoT traffic. CNN reaches only 88.36% (binary) and 87.22% (multi-class) accuracy, while SVM records the lowest among all, with 85.29% and 84.06%, respectively. These results underscore the importance of combining both advanced attention mechanisms and adversarial training for effective intrusion detection.