## 1 Introduction
### Related Works  
Numerous research efforts have been devoted to enhancing intrusion detection in IoT networks using a variety of machine learning (ML), deep learning (DL), and optimization techniques. Given the rapid proliferation of IoT devices and the growing diversity of cyber threats, recent studies have focused on developing intelligent and adaptive IDS capable of detecting both known and emerging attacks. These approaches range from conventional neural networks (NNs) to advanced frameworks incorporating attention mechanisms, deep reinforcement learning (DRL), and hybrid architectures. The availability of modern and realistic datasets, such as CIC-IoT-2023 and ToN-IoT, has further enabled more reliable benchmarking and performance evaluation of these models. The following studies illustrate key advancements in this domain and provide context for the development of our proposed transformer generative adversarial network (GAN)-based IDS framework. In [20], the authors proposed a federated edge learning framework for IoT intrusion detection using the CIC-IoT-2023 dataset. Their approach leverages a federated deep neural network trained with data preprocessing techniques such as normalization and balancing to detect large-scale cyberattacks. The method achieved a high accuracy of 99.00%, demonstrating its effectiveness, although its reliance on centralized aggregation and lack of adversarial learning may limit robustness in more complex threat scenarios. In [21], the authors conducted a comparative study of six DL models, including CNN, LSTM, and hybrid architectures like CNN + LSTM, for anomaly detection using the CSE-CIC-IDS2018 dataset. Their results indicated that while hybrid models achieved high accuracy (>98%), standalone DNN, CNN, and RNN models were more efficient in inference time. However, the study focused on older datasets and lacked advanced optimization strategies, such as evolutionary or adversarial techniques, for real-world IoT adaptability. In [22], the authors introduced a modified transformer neural network (MTNN) to enhance intrusion detection in IoT networks. Using the ToN-IoT dataset, their transformer-based IDS outperformed LSTM and RNN baselines in accuracy, precision, recall, and F score, highlighting the transformer's effectiveness for capturing temporal patterns. Unlike our proposed approach, the MTNN did not incorporate synthetic data generation or multi-objective optimization to handle class imbalance and model generalization. In [23], the authors explored anomaly detection using temporal convolutional networks and U-Net models. Although their approach achieved strong performance on the CSE-CIC-IDS2018 and KDD99 datasets (up to 97% accuracy), the study also revealed the limitations of older datasets and emphasized the risk of overfitting. Their work supports the importance of using realistic and diverse datasets, like CIC-IoT-2023, as employed in our research. In [24], the authors proposed TA-NIDS, a transferable intrusion detection framework based on DRL. This model emphasizes adaptability and sample efficiency, allowing it to perform well even with limited training data. While the method shows promising transferability across datasets including CIC-IoT-2023, it lacks the generative capabilities and structured attention mechanisms that our Transformer-GAN hybrid leverages for deeper feature learning and synthetic augmentation. In [25], the authors implemented an LSTM-based intrusion detection system using the CIC-IoT-2023 dataset. Their model achieved strong results (98.75% accuracy and 98.59% F1-score), showcasing LSTM's ability to capture temporal dependencies. However, the model's reliance solely on sequential learning without adversarial training or optimization algorithms restricts its generalization to more imbalanced or evolving threat scenarios. In [26], the authors proposed a hybrid ML approach for classifying web-based attacks using TPOT and genetic algorithms for parameter optimization. Their best-performing classifier, Gradient Boosting, achieved an accuracy of 95%.