## 3 Results

Table 2: _Cont._Each of these metrics provides distinct and complementary insights into different aspects of model performance, covering classification quality, stability, optimization dynamics, and computational efficiency. Accuracy measures the overall proportion of correctly classified samples among all input samples. It is a basic but important metric that reflects how well the model distinguishes between benign and malicious traffic in the dataset. While high accuracy is desirable, it should be interpreted carefully in imbalanced datasets, as it can sometimes be misleading if one class dominates. In this study, accuracy is calculated based on Equation (17), where true positives and true negatives are summed and divided by the total number of instances:  
\[Accuracy=\frac{true\ positive+true\ negative}{true\ positive+true\ negative+false\ positive+false\ negative} \tag{17}\]  
The F1-Score provides a more balanced evaluation by considering both precision and Recall simultaneously. It is particularly important in imbalanced datasets, where achieving a high F1-Score indicates that the model not only correctly identifies true positives but also minimizes false positives and false negatives. A high F1-Score reflects the model's robustness in handling diverse intrusion patterns without bias toward any specific class. The F1-Score is computed according to Equation (18) as the harmonic mean of precision and Recall:  
\[F1-Score=2\times\frac{Precision\times Recall}{Precision+Recall} \tag{18}\]  
The AUC evaluates the model's ability to discriminate between classes across all possible classification thresholds. AUC provides a threshold-independent measure, where a value close to 1 indicates excellent discriminative capability and a value near 0.5 suggests random guessing. Higher AUC values demonstrate that the model is consistently better at distinguishing between benign and malicious instances, regardless of the specific decision boundary chosen. The AUC metric is mathematically expressed by integrating the ROC curve across different thresholds, as shown in Equation (19):  
\[AUC=\int_{0}^{1}ROC(t)dt \tag{19}\]  
where \(ROC(t)\) is ROC curve at threshold \(t\). Variance analysis was also employed to assess the stability and consistency of the model's performance across multiple independent runs. Lower variance values indicate that the model behaves predictably and does not heavily depend on specific random initializations or particular subsets of training data. In high-stakes security applications like IoT intrusion detection, low variance ensures that the model delivers reliable performance in different deployment scenarios without unpredictable fluctuations. The convergence trend analysis was conducted to observe how quickly and steadily the model's loss function decreases during the training process. A smooth and rapid convergence toward lower loss values typically reflects a well-optimized model and a healthy training regime. Monitoring convergence behavior also helps to detect issues such as overfitting, vanishing gradients, or instability, allowing for early corrective adjustments in hyper-parameter settings or training strategies. Finally, the execution time was measured to evaluate the computational efficiency of the proposed framework. This metric quantifies the total time required for model training and testing phases, offering insights into the practical deployability of the system. Faster execution times without compromising detection accuracy are particularly valuable for IoT networks, where resource constraints and real-time responsiveness are critical. The careful tuning of hyper-parameters is a crucial factor that significantly influences the training stability, convergence behavior, and final performance of ML and DL models. Hyper-parameters such as learning rate, batch size, hidden layer sizes, and dropout rates govern how effectively a model learns from the training data and how well it generalizes to unseen scenarios.