## 4 Discussion  
In this section, we conduct an in-depth discussion of the experimental outcomes by analyzing three critical aspects of the proposed framework: computational complexity and execution time, statistical significance of the results, and the stability of the model under real-world data distributions. These aspects are especially relevant in the context of the CIC-IoT-2023 dataset, which consists of high-dimensional and imbalanced traffic patterns across multiple types of malicious behaviors. The complexity of IoT attack vectors and the heterogeneous feature spaces pose unique challenges for intrusion detection systems, making it imperative to validate not only detection accuracy but also the efficiency and robustness of the learning process. The first part of this analysis focuses on the runtime efficiency and computational burden of the evaluated models, particularly concerning training convergence and inference speed. As IoT systems typically operate under resource-constrained environments, models with lower training overhead and faster execution times are more suitable for practical deployment. The second component of our discussion implements statistical hypothesis testing using independent two-sample \(t\)-tests to verify whether the observed performance improvements of the transformer-GAN over baseline methods are statistically significant. This step ensures that the reported gains are not merely due to random fluctuations in training or dataset artifacts. Finally, we examine model stability through standard deviation analysis of key metrics such as accuracy, F1-score, and AUC across multiple training runs. A low standard deviation would indicate a stable model that performs consistently across different initializations and data shuffles, a vital property in adversarial and dynamic IoT threat landscapes. Figure 10: Learning curves of RMSE versus epochs for various models TON_IoT dataset: (**a**) Binary classification; (**b**) Multi-Class classification. Table 6 compares the computational efficiency of various models by reporting their runtime (in seconds) required to surpass specific accuracy thresholds (35%, 55%, 75%, and 95%) under the multi-class intrusion detection task. This evaluation provides insight into each model's learning speed and convergence efficiency, which are particularly important when deploying detection frameworks in latency-sensitive or resource-constrained IoT environments. The results clearly demonstrate the superiority of the transformer-GAN in terms of both convergence speed and computational cost. It achieves 35% accuracy in just 16 s, 55% in 59 s, and surpasses 75% in under 100 s, reaching the highest observed accuracy level of over 95% in 157 s. Additionally, we included the Transformer and GAN combination (without INSBBO) in Table 6 to further assess its computational efficiency relative to other baselines. In contrast, the Transformer and GAN combination requires 259 s to surpass 75% accuracy and, similar to other baselines, does not reach the 95% threshold within the observed training window. Meanwhile, the standalone transformer requires over 300 s to approach similar levels of performance. Other models like GAN, LSTM, CNN, and SVM show significantly slower learning curves, SVM, for instance, takes over 900 s to reach 75% accuracy, and none of them exceed the 95% mark. This reinforces transformer-GAN's advantage not just in final performance, but also in computational efficiency. From an architectural perspective, this efficiency gain can be attributed to the synergistic integration of the transformer's attention mechanism and GAN's generative capabilities, further enhanced through INSBBO-based hyper-parameter optimization. These factors enable faster convergence while avoiding overfitting, even under imbalanced and multi-modal data as seen in the CIC-IoT-2023 dataset.