## 4 Discussion
Rejecting this hypothesis supports the claim that the proposed model's improvements are not due to random fluctuations or sample biases but are statistically robust. We set the significance level at \(\alpha\) = 0.01, meaning any \(p\)-value below this threshold would confirm that the difference is statistically significant with at least 99% confidence. This threshold ensures strict criteria, avoiding false positives when comparing high-performance models on complex datasets like CIC-IoT-2023. This dataset includes a wide range of attack types and class imbalances, which could otherwise cause variability in model performance. Conducting \(t\)-tests under these conditions strengthens the empirical foundation of our evaluation, making the comparison both rigorous and generalizable. Table 7 illustrates statistical significance results (\(p\)-values) from \(t\)-tests comparing transformer-GAN to baseline models in both binary and multi-class classification. All \(p\)-values for both binary and multi-class comparisons are well below \(\alpha\) = 0.01 threshold, ranging from 0.0006 to 0.000005 in binary classification, and 0.0009 to 0.00008 in multi-class classification. These values strongly confirm that the performance differences between transformer-GAN and all other models are statistically significant. The smallest \(p\)-values are observed in comparisons against SVM and LSTM, indicating the largest performance gaps. Even against the standalone transformer and GAN models, the differences are statistically validated with high confidence. This outcome supports the conclusion that the superior performance of transformer-GAN is not incidental but instead stems from fundamental architectural advantages, including its hybrid learning strategy, attention-based temporal modeling, and optimized configuration via INSBBO. In high-stakes IoT environments,  
where decisions depend on reliably detecting real threats, statistical confidence in performance improvements is crucial. These results reinforce the credibility and practicality of deploying transformer-GAN as a state-of-the-art detection mechanism in both binary and multi-class intrusion detection tasks. Table 8 presents the standard deviation values of the classification performance across multiple experimental runs for both binary and multi-class tasks. This table evaluates the stability and robustness of each model when exposed to different data shuffling, initializations, and random perturbations. Lower standard deviation indicates that the model's results are consistent across different trials, while higher values suggest more volatile and less reliable performance. Assessing standard deviation is crucial because real-world IoT environments, like those reflected in the CIC-IoT-2023 dataset, inherently involve dynamic and unpredictable traffic patterns. Attack behaviors vary over time, and small fluctuations in training or input data can significantly impact poorly generalized models. Thus, models intended for intrusion detection must demonstrate not only high mean performance but also strong stability across varying conditions. By examining standard deviation, we ensure that the proposed transformer-GAN is not merely achieving high results on select runs but is consistently reliable across the board. Analyzing Table 8, the transformer-GAN model exhibits exceptionally low standard deviation values (0.0000365 for binary and 0.0006325 for multi-class classification) confirming its outstanding stability. In contrast, baseline models such as CNN and SVM suffer from much higher variance (e.g., over 4.32 and 7.21, respectively, in multi-class tasks), suggesting high sensitivity to initial conditions and training noise. Even the standalone transformer shows a notable increase in variability compared to the hybrid model. This result further highlights the advantage of combining the transformer's attention mechanisms with GAN's feature diversity and INSBBO's optimization capabilities, producing a model resilient to IoT intrusion data's dynamic, noisy, and complex nature.