## 3 Results
Among the baselines, the hybrid model without INSBBO (Transformer and GAN) ranked second in all metrics, reinforcing the individual strength of both core components. The standalone Transformer and GAN models followed closely but showed reduced performance, especially in multi-class detection, with GAN performing better in F1-score but slightly lower in AUC. Traditional models such as LSTM, CNN, and SVM lagged noticeably in both binary and multi-class tasks, indicating their limited scalability in more heterogeneous environments. Overall, the strong and consistent performance of our model across two diverse datasets highlights its adaptability and reliability for real-world IoT intrusion detection scenarios. To complement the numerical performance metrics, we provide a visual comparison of the detection capabilities of various models using ROC curves on the TON_IoT dataset. In the binary classification setting (Figure 9a), the Transformer-GAN (INSBBO) achieves a near-perfect curve that closely follows the top-left boundary of the plot, indicating extremely high sensitivity across all specificity levels. Compared to other models, it demonstrates the sharpest rise and earliest saturation, confirming its superior ability to distinguish malicious traffic from benign flows with minimal false positives. The Transformer and GAN models  
Figure 8: Learning curves of RMSE versus epochs for various models in CIC-IoT-2023 dataset: (a) Binary classification; (b) Multi-Class classification. also perform well but remain slightly below the proposed model, while LSTM, CNN, and RNN exhibit flatter curves with larger areas under the diagonal, reflecting their relatively weaker separation capabilities. In multi-class classification (Figure 9b), the advantage of the proposed architecture remains evident. The ROC curve of the Transformer-GAN (INSBBO) dominates those of all baselines, showing consistently higher sensitivity across varying thresholds. This suggests that not only does the model excel at detecting whether traffic is malicious, but it also reliably differentiates between multiple attack types. The Transformer and LSTM models again rank next in performance, while CNN, GAN, and especially RNN display degraded discriminative behavior in this more complex classification setting. These findings visually reinforce the numerical metrics presented earlier in Table 5 and highlight the generalization capability of our model in heterogeneous and dynamic IoT environments. To further investigate the learning efficiency and convergence behavior of the evaluated models, we plot the RMSE learning curves over training epochs on the TON_IoT dataset. As depicted in Figure 10, the plots show how quickly and effectively each model minimizes its prediction error during training for both binary and multi-class classification tasks. Lower RMSE values across fewer epochs indicate better convergence speed and generalization behavior of the model. In the binary classification setting (Figure 10a), the proposed Transformer-GAN (INSBBO) demonstrates the fastest and most stable convergence trajectory, reaching an RMSE below 5 in less than 100 epochs. In contrast, other  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Algorithm**} & \multicolumn{2}{c}{**Accuracy (\%)**} & \multicolumn{2}{c}{**F1-Score (\%)**} & \multicolumn{2}{c}{**AUC (\%)**} \\ \cline{2-7}  & **Binary** & **Multi-Class** & **Binary** & **Multi-Class** & **Binary** & **Multi-Class** \\ \hline Transformer-GAN (INSBBO) & 99.87 & 99.75 & 99.72 & 99.68 & 99.96 & 99.84 \\ \hline Transformer and GAN & 96.93 & 94.90 & 96.32 & 95.97 & 97.14 & 95.96 \\ \hline Transformer & 94.79  
models such as LSTM, CNN, and GAN converge more slowly and stabilize at noticeably higher RMSE values. SVM, in particular, shows limited convergence capability, retaining high error levels even after 300 epochs. These trends confirm the efficiency of adversarial and transformer-based learning dynamics in capturing the underlying data distribution quickly and with minimal error.