## 4 Results and Discussion  
For the experiential evaluation, a laptop computer was used, with specifications as shown in Table 4.  
The ANN model achieved an overall accuracy of 99.20%. The detailed classification report is depicted in Table 5. Overall, the ANN was trained using a batch size of 32, which provides a good balance between computational efficiency and model convergence. The binary cross-entropy loss function was chosen as it is well-suited for binary classification tasks, especially when the output layer uses a sigmoid activation function to predict class probabilities. The model was optimized using the Adam optimizer, which adapts learning rates during training and is known for its robust performance across various deep learning applications. Although the model was initially set to train for 10 epochs, training continued until the model reached a minimum loss threshold, ensuring convergence and preventing overfitting. The SVM model achieved an overall accuracy of 96.80%. The detailed classification report is depicted in Table 6.  
The final ensemble model achieved an overall accuracy of 99.40%. The detailed ensemble model classification report is depicted in Table 7. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Label** & **Precision (\%)** & **Recall (\%)** & **F1-Score (\%)** \\ \hline
0, Normal & 95 & 94 & 93 \\ \hline
1, Anomaly & 93 & 96 & 94 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: The detailed classification report on SVM. \begin{table}
\begin{tabular}{c c} \hline \hline
**Component** & **Specification** \\ \hline Processor (CPU) & Intent Core i5-72000 2.5 GHz \\ \hline Graphics card (GPU) & NVIDIA GeForce 2 GB (NVIDIA, Santa Clara, CA, USA) \\ \hline RAM & 16 GB DDR4 \\ \hline Integrated development environment & Jupyter Notebook \\ \hline Libraries used & TensorFlow, Scikit-learn, Pandas, Numpy etc. \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Device specifications. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Label** & **Precision (\%)** & **Recall (\%)** & **F1-Score (\%)** \\ \hline
0, Normal & 98 & 97 & 97 \\ \hline
1, Anomaly & 98 & 99 & 97 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The detailed classification report on ANN. The ensemble model's superior performance (99.40%) over standalone classifiers demonstrates the strength of combining complementary learning strategies. The ROC curve presented in Figure 8 illustrates the performance of the proposed intrusion detection model by plotting the True Positive Rate (Recall) against the False Positive Rate (FPR) at various threshold levels. The orange curve rises steeply toward the top-left corner, indicating that the model effectively distinguishes between the anomaly and non-anomaly classes. A near-vertical ascent followed by a flat plateau along the top signifies high sensitivity with minimal false positives. The area under the ROC curve (AUC) is close to 1.0, suggesting excellent classification performance and a strong ability to discriminate between classes. On the other hand, the precision-recall curve shown in Figure 9 highlights the balance between precision and recall for the same classifier. The curve maintains a high precision \begin{table}
\begin{tabular}{c c c c} \hline
**Label** & **Precision (\%)** & **Recall (\%)** & **F1-Score (\%)** \\ \hline
0, Normal & 99 & 98 & 98 \\ \hline
1, Anomaly & 99 & 99 & 98 \\ \hline \end{tabular}
\end{table} 
Table 7: The detailed classification report on the final ensemble model. Figure 8: The ROC curve. Figure 7: Confusion matrix of the final ensemble. level (close to 1.0) across nearly all recall values, except for a minor drop near the maximum recall threshold. This pattern demonstrates that the model can identify almost all actual anomalies without significantly sacrificing precision, even under imbalanced class distributions. The steep slope at the end indicates a trade-off between precision and recall at extreme thresholds, which is common in high-performing binary classifiers dealing with rare positive classes such as intrusions.