## 3 Results  
All experiments conducted in this study were implemented using Python 3.8, due to its versatility and extensive ecosystem of DL and optimization libraries. The proposed transformer-GAN hybrid model was developed using the TensorFlow 2.8 and Keras frameworks, which offer flexible APIs for building and training complex neural network architectures. To construct and optimize the INSBBO algorithm, custom Python modules were written, leveraging NumPy, SciPy, and Pandas for numerical computation, data manipulation, and optimization routines. All preprocessing operations, including data merging, label encoding, and normalization, were handled using the Scikit-learn library, which also supported the evaluation metrics computations. The experiments were executed on a high-performance workstation equipped with an NVIDIA RTX 3090 GPU with 24 GB VRAM, 128 GB RAM, and an Intel Core i9-12900K processor, enabling efficient training of large-scale Transformer and GAN models. This hardware configuration ensured that the computational demands of training, hyper-parameter optimization, and Pareto-based solution sorting under the INSBBO framework could be handled effectively. The complete implementation pipeline was designed to maximize reproducibility and scalability, facilitating rigorous comparative analysis across both binary and multi-class intrusion detection tasks based on the CIC-IoT-2023 and the TON_IoT dataset. To comprehensively evaluate the effectiveness of the proposed transformer-GAN framework, we compared its performance against several widely recognized baseline models, namely basic transformer, GAN, CNN, LSTM, and SVM. These baselines were selected to represent a diverse range of ML and DL paradigms commonly applied in intrusion detection tasks. By including both traditional ML methods and modern deep architectures, the comparative analysis ensures a fair and meaningful assessment of the hybrid model's advantages in various detection scenarios, covering both binary and multi-class classification tasks. Each baseline was chosen based on its historical relevance and technical characteristics. The standalone Transformer was included to highlight the added benefits achieved by introducing adversarial learning to feature extraction. Similarly, the GAN model was evaluated independently to demonstrate the superiority of the proposed synergistic integration over using generative adversarial training alone. CNN and LSTM were selected as they are established DL models for sequence and spatial data processing, respectively, offering strong baselines for feature extraction and temporal dependency modeling. SVM, a classical and robust ML technique, was incorporated to benchmark the hybrid model against traditional classifiers that excel in small to medium-scale intrusion detection problems. This carefully selected set of baselines enables a thorough validation of the proposed model's robustness, generalization ability, and performance superiority in the challenging context of IoT security. To rigorously evaluate the performance of the proposed transformer-GAN model and baseline methods, several widely accepted metrics were employed. These include accuracy, F1-score, area under the curve (AUC), variance, convergence trend, and execution time. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Attack Class** & \begin{tabular}{c} **Generated** \\ **Samples** \\ \end{tabular} & \begin{tabular}{c} **Correctly** \\ **Classified (\%)** \\ \end{tabular} & \
\begin{tabular}{c} **Class** \\ **Coverage (\%)** \\ \end{tabular} &
\begin{tabular}{c} **Average** \\ **Confidence** \\ \end{tabular} \\ \hline Spoofing & 1000 & 93.90 & 97.00 & 0.9 \\ \hline SQL Injection & 1000 & 92.50 & 96.20 & 0.89 \\ \hline Brute Force & 1000 & 95.10 & 98.90 & 0.91 \\ \hline Web Attacks & 1000 & 94.40 & 97.40 & 0.9 \\ \hline Mirai & 1000 & 91.80 & 95.70 & 0.88 \\ \hline \hline \end{tabular}
\end{table}