## 3 Results  
All experiments were conducted using a workstation equipped with an Intel Core i9-12900K CPU, 64 GB of RAM, and an NVIDIA RTX 3090 GPU with 24 GB of VRAM, running on Ubuntu 22.04 LTS. The implementation of the proposed transformer-GAN-AE framework and all baseline models was carried out in Python 3.9, utilizing PyTorch 2.0.1 as the primary DL library. Data preprocessing, scaling, and augmentation were performed using Pandas 1.5.3 and Scikit-learn 1.2.2, while numerical operations relied on NumPy 1.24.2. Visualization of results was handled using Matplotlib 3.7.1 and Seaborn 0.12.2. Experiments were conducted independently on three benchmark datasets--WUSTL-IIoT-2021, Edge-IIoTset, and TON_IoT, using a consistent train-validation-test split of 70%, 15%, and 15%, respectively. Each experiment was repeated 25 times, and the average results are reported to ensure statistical robustness. To validate the effectiveness of the proposed Transformer-GAN-AE framework, we conducted comprehensive comparisons against a set of well-established baseline models spanning generative learning, deep neural architectures, and gradient-based classifiers. These include the standard GAN, convolutional neural network (CNN), deep belief network (DBN), time-series transformer (TST), bidirectional encoder representations from transformers (BERT), and extreme gradient boosting (XGBoost). This diverse selection ensures a thorough and fair evaluation across multiple architectural paradigms, covering both DL and ML perspectives. The inclusion of GAN as a baseline enables a direct comparison with the generative component of our framework, isolating the value added by the integrated AE and Transformer modules. CNN is widely adopted in intrusion detection tasks due to its efficiency in capturing spatial hierarchies and local patterns within feature vectors, making it a strong convolutional benchmark. DBN, as a classical unsupervised deep architecture, allows us to evaluate performance from a probabilistic representation learning perspective, particularly under noisy or sparse data conditions. Its hierarchical structure and pre-training mechanism provide useful insight into how deep generative models handle IIoT data. For temporal modeling, we include TST and BERT, both of which leverage the self-attention mechanism but differ in complexity and design philosophy. TST represents a lightweight temporal transformer tailored for time series forecasting and sequence classification, making it highly relevant for capturing IIoT traffic patterns. BERT, on the other hand, brings contextualized embeddings and bidirectional attention modeling to the task, offering a powerful comparison point for language-model-inspired architectures applied to security data. Finally, XGBoost is selected as a non-deep learning benchmark due to its exceptional performance in many tabular intrusion detection problems. As a powerful ensemble method with gradient boosting, XGBoost serves as a strong classical baseline for evaluating the added value of deep and hybrid models. The inclusion of these models provides a holistic benchmark space to assess generalization, anomaly sensitivity, and robustness across architectures. To assess the effectiveness of the proposed Transformer-GAN-AE architecture and baseline models in detecting intrusions, we employed several widely recognized performance metrics. First, accuracy was used to measure the overall correctness of the model's predictions. As shown in Equation (31), accuracy is defined as the ratio of correctly classified instances (_true positives_ and _true negatives_) to the total number of predictions. While accuracy provides a general sense of performance, it can be misleading in highly imbalanced datasets, where the dominant class skews the metric. However, in this study, it was still a useful indicator when combined with more sensitive measures.