## 5 Conclusions  
In this paper, we proposed a novel hybrid intrusion detection framework for IoT environments by synergistically integrating transformer networks with GANs. The transformer component was utilized to capture complex temporal and contextual dependencies within IoT traffic sequences, while the GAN component was employed to enhance feature diversity and mitigate class imbalance. Furthermore, a novel INSBO algorithm was incorporated to fine-tune the hyper-parameters and internal configurations of the hybrid model, ensuring optimized learning dynamics. The effectiveness of the proposed system was systematically validated through extensive experiments on the CIC-IoT-2023 and TON_IoT dataset, which contains a rich variety of real-world IoT traffic patterns and attack scenarios. The experimental design included comparisons with strong baseline models such as transformer and GAN (without INSBO), standalone transformer, GAN, LSTM, CNN, and SVM across both binary and multi-class classification tasks. To ensure a rigorous evaluation, multiple performance aspects were analyzed, including accuracy, F1-score, AUC, runtime efficiency, and model stability. Statistical hypothesis testing using independent two-sample \(t\)-tests and standard deviation analysis further validated the reliability and robustness of the proposed approach under diverse and dynamic IoT conditions. This comprehensive evaluation strategy ensured that the reported improvements were statistically meaningful and practically relevant for deployment in real-world security applications. The experimental results consistently demonstrated the superiority of the proposed transformer-GAN framework. The transformer-GAN achieves a multi-class classification accuracy of 99.67%, with an F1-score of 99.61%, and an area under the curve (AUC) of 99.80% in the CIC-IoT-2023 dataset, and achieves 98.84% accuracy, 98.79% F1-score, and 99.12% AUC on the TON_IoT dataset. In addition, the model showed exceptional efficiency in terms of runtime, reaching high accuracy thresholds substantially faster than traditional DL models. Standard deviation analysis revealed minimal performance variance across multiple runs, indicating strong stability and resilience to data perturbations. Furthermore, independent \(t\)-test results confirmed the statistical significance of the observed improvements, with all \(p\)-values falling well below the 0.01 threshold. Beyond numerical performance, qualitative analysis highlighted that the combination of the transformer's attention mechanisms and GAN's adversarial training yielded robust feature representations capable of accurately detecting a wide range of sophisticated intrusion patterns in the CIC-IoT-2023 dataset. The computational efficiency achieved by the model makes it particularly attractive for practical deployment in resource-constrained IoT environments, such as smart homes, industrial systems, and critical infrastructure networks. The consistent success of the proposed approach across binary and multi-class classification tasks suggests that combining attention-driven sequence modeling with adversarial feature learning is highly effective for complex anomaly detection problems. Furthermore, the significant improvements observed in statistical significance testing and low standard deviation analysis indicate that the model is not only powerful but also robust against variations in data distribution and initial conditions. These findings highlight the potential of advanced hybrid architectures, combined with evolutionary optimization, to set new benchmarks for securing next-generation IoT ecosystems against increasingly sophisticated cyber threats. For future work, one promising direction would be to explore the integration of more advanced generative models, such as diffusion models or variational autoencoders (VAEs), into the current transformer-GAN framework.