## 3 Results
Inadequate or suboptimal hyper-parameter settings may lead to issues such as overfitting, underfitting, slow convergence, or instability during optimization, ultimately degrading classification performance. Particularly for complex hybrid models like the proposed transformer-GAN, the role of precise hyper-parameter calibration becomes even more important, as multiple interconnected components must be simultaneously optimized to achieve robust intrusion detection capabilities across diverse IoT attack scenarios. For the proposed transformer-GAN architecture, hyper-parameter optimization was conducted using the INSBBO algorithm. INSBBO was employed to jointly tune critical factors such as the learning rate, dropout rate, attention configurations, and feedforward dimensions, ensuring that the hybrid model achieves optimal balance between exploration and exploitation during training. The INSBBO method was particularly advantageous because of its ability to adaptively explore complex, nonlinear search spaces, identify Pareto-optimal parameter combinations, and simultaneously optimize multiple performance objectives including detection accuracy and model efficiency. In contrast, for the baseline models, hyper-parameter calibration was performed using a standard grid search approach. The grid search method systematically explores a manually specified set of hyper-parameter values, evaluating the model's performance for each possible combination to identify the optimal configuration. Although grid search is computationally expensive, it is widely adopted due to its simplicity and exhaustiveness, making it suitable for models with moderate-sized hyper-parameter spaces. In our experiments, key parameters such as learning rates, hidden sizes, batch sizes, kernel sizes, and regularization terms were fine-tuned using this method to ensure a fair and competitive baseline evaluation. The hyper-parameter settings selected for each model are detailed in Table 3, covering both the proposed transformer-GAN architecture and all baseline methods. For the transformer-GAN, critical training parameters were finely tuned through the INSBBO optimizer. Specifically, a learning rate of 0.001 and a batch size of 32 were selected, balancing convergence speed and stability. The feed forward hidden size was set at 2048, consistent with standard transformer architectures to ensure rich feature representations. The number of attention heads was configured at 10, and both the encoder and decoder layers were set to 6, forming a moderately deep architecture suitable for complex IoT intrusion patterns. A dropout rate of 0.1 and weight decay of 0.01 were applied for regularization, while GELU was chosen as the activation function for smoother gradient flows. Additionally, INSBBO-specific hyper-parameters included a population size of 100, and a mutation rate of 0.06, ensuring effective evolutionary search. For the standalone Transformer baseline, a slightly deeper structure was adopted, with 8 encoder and 8 decoder layers, 12 attention heads, a larger batch size of 64, and a slightly lower learning rate of 0.002, aiming to leverage deeper sequence modeling capabilities. For the GAN baseline, hyper-parameters were tuned to fit a generative adversarial setup with relatively lightweight architectures. A learning rate of 0.004 and a batch size of 64 were employed, with 64 neurons per hidden layer and ReLU activation for nonlinearity. Momentum was set at 0.05 to balance stability and convergence speed during SGD-based optimization. The LSTM model used a higher learning rate of 0.04, a batch size of 64, and a recurrent dropout rate of 0.3 to mitigate overfitting in sequential data modeling. It consisted of 8 hidden layers, each with 64 neurons, and used a combination of tanh and sigmoid activations to capture temporal dependencies effectively. The CNN baseline was configured with 6 convolutional layers using a 3 \(\times\) 3 kernel size and max pooling (2 \(\times\) 2), with 28 neurons in dense layers and a regularization parameter of 1 to prevent overfitting.