## 4 Discussion  
The proposed Transformer-GAN-AE framework demonstrates substantial potential beyond numerical accuracy metrics. Its hybrid architecture not only excels in detection capability but also integrates three essential characteristics (adaptability, generative robustness, and temporal modeling) that make it particularly suited for deployment in real-world IIoT and edge environments. Unlike monolithic models that often overfit to specific data patterns, this approach leverages synergy between components: the transformer encodes long-term sequence behavior, the GAN balances class distributions, and the autoencoder denoises input to enhance anomaly sensitivity. Together, these elements form a resilient and versatile detection mechanism suitable for evolving industrial threats. While previous sections focused primarily on detection performance, this section extends the evaluation by addressing critical practical considerations: execution time, statistical robustness, and model consistency. These dimensions are vital for transitioning from lab-scale experimentation to industrial deployment, where constraints on computation, response time, and reliability are non-negotiable. In Edge or embedded IIoT systems, even a few seconds of additional delay or instability across runs can compromise the trustworthiness of a security framework. We first examine the execution time required by each model. Efficiency is crucial in time-sensitive IIoT applications, especially in scenarios such as predictive maintenance, anomaly mitigation, or real-time threat response. A model that performs well but takes too long to converge or respond may not be viable in edge environments with limited computational resources. Next, we consider the results of a statistical t-test to determine whether the observed performance improvements of the proposed model are statistically significant. This analysis adds rigor to the evaluation, confirming that gains are not artifacts of randomness or dataset bias. Lastly, we assess variance across multiple experimental runs to evaluate the consistency and reliability of each model. High-performing systems must not only achieve strong average performance but must do so consistently under different initializations, random seeds, and training conditions. Low variance suggests robustness and reproducibility--two attributes that are essential for safety-critical systems, particularly in industrial automation, smart grids, or autonomous monitoring environments. These three additional dimensions together provide a holistic view of the practicality, deployability, and stability of Transformer-GAN-AE in real-world industrial cybersecurity ecosystems. Table 4 presents a comparative runtime analysis of all evaluated models under different RMSE convergence thresholds on the WUSTL-IIoT-2021 dataset. The table reports the time (in seconds) each model requires to reach RMSE values below 15, 10, 5, and 2.5. This analysis offers insight into the efficiency and scalability of each algorithm when deployed  
in scenarios that demand varying levels of precision or convergence quality. It highlights not only the final performance but also the speed with which each model can achieve meaningful predictive accuracy. The Transformer-GAN-AE model exhibits a significant advantage in runtime efficiency across all thresholds. It reaches RMSE < 15 in just 15 s and converges to RMSE < 2.5 in only 128 s--outperforming all other models by a large margin. BERT, TST, and DBN show relatively moderate efficiency, but their inability to reach RMSE < 5 or 2.5 within reasonable time frames underlines the limitations of their training dynamics and optimization methods. In contrast, models such as CNN, GAN, and XGBoost fail to reach thresholds below RMSE < 10 altogether, indicating poor convergence behavior under higher precision demands. These findings reinforce the architectural and optimization benefits of the proposed framework. The use of the IChOA optimizer accelerates convergence and avoids stagnation in local minima.