## 3 Results
The superior metrics of Transformer-GAN are attributed to its deep feature learning capabilities, resilience to class imbalance via adversarial synthesis, and its optimized architecture, which collectively lead to robust and discriminative representations. Figure 5 presents a radar plot visualization of the classification accuracies achieved by different models across both binary and multi-class intrusion detection tasks. The radar charts offer an intuitive, comparative view of model performances, enabling quick visual identification of relative strengths and weaknesses. The closer a point is to the outer edge of the radar plot, the higher the accuracy achieved by the corresponding model, thus highlighting performance disparities more clearly than tabular data alone. From the radar plots, it is evident that the proposed transformer-GAN (INSBBO) consistently dominates in both binary and multi-class scenarios, forming the outermost vertex in both subplots. In the binary classification plot, transformer-GAN is distinctly separated from other methods, confirming its superior performance margin compared to the standalone transformer and GAN, transformer, GAN, LSTM, CNN, and SVM. The standalone transformer and \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) & 99.76 & 99.67 & 99.63 & 99.61 & 99.91 & 99.80 \\ Transformer and GAN & 96.75 & 94.89 & 96.20 & 95.68 & 96.89 & 95.70 \\ Transformer & 95.28 & 94.49 & 94.98 & 94.31 & 95.46 & 94.10 \\ GAN & 92.32 & 91.14 & 92.04 & 91.02 & 92.96 & 91.96 \\ LSTM & 90.48 & 89.60 & 90.21 & 89.23 & 90.75 & 89.51 \\ CNN & 88.36 & 87.22 & 88.30 & 86.94 & 88.65 & 87.66 \\ SVM & 85.29 & 84.06 & 84.67 & 83.76 & 85.32 & 84.52 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparative performance of all models in binary and multi-class intrusion detection (CIC-IoT-2023 dataset). GAN follows as the second-best performer, while traditional models like CNN and SVM cluster toward the center, indicating comparatively lower accuracy. A similar trend is observed in the multi-class radar plot, although the performance gap slightly narrows among baseline models. Nevertheless, the proposed transformer-GAN maintains a clear advantage, demonstrating its robustness not only in simpler binary distinctions but also in more complex multi-class intrusion detection scenarios. Figure 6 provides a visual comparison of the models' performance based on two critical evaluation metrics: F1-score and AUC. Each bar represents the value obtained by a specific model across the two metrics, allowing a direct visual comparison of their effectiveness. The height of each bar corresponds to the achieved percentage, with higher bars indicating superior performance in terms of precision-recall balance (F1-score) and discriminative ability (AUC). From the bar charts, it is clear that the proposed transformer-GAN model consistently outperforms all baseline methods across both binary and multi-class tasks for both F1-score and AUC metrics. In binary classification, transformer-GAN achieves near-perfect values close to 100% in both F1-score and AUC, substantially higher than the others. A similar trend is observed in the multi-class results, although performance gaps between the models slightly narrow. Nonetheless, transformer-GAN maintains a significant advantage, confirming its robustness in both scenarios. Figure 7 illustrates the ROC curves for all evaluated models, comparing their classification capabilities across binary and multi-class intrusion detection tasks in the CIC-IoT-2023 dataset. Each curve depicts the trade-off between the true positive rate (sensitivity) and the false positive rate (1-specificity) at various decision thresholds.