## 4 Results and Discussion
### Machine Learning Efficiency Metrics
As presented in Figure 6, the figure includes related studies that evaluated the test and training times. The results show that the filter-based feature selection method that we used can significantly reduce the time and complexity compared with related studies without compromising detection accuracy, which is beneficial for lightweight IoT devices and low storage. Such improvements in training/testing time are crucial for predicting real-life intrusions, accelerating the IDS function to guarantee the rapid detection of attacks, and ensuring that the IDS has fast data stream monitoring capabilities. It has been reported that a practical real-time IDS is vital and desirable for detecting intrusions in high-speed IoT networks with large volumes of data [7]. This further confirms the suitability of the generated synthetic IoT dataset for ML classification training and the possibility of replacing real-life datasets. Moreover, the results of this study show that it is possible to use the proposed methodology to generate a new synthetic dataset containing a broader number of attacks without requiring months of recording and large storage for collecting attack datasets. Additionally, the generated dataset after feature selection can be applied to an ML-based IDS in a lightweight IoT environment without affecting the accuracy of real-time updates and communication between IoT devices. The final results showed similar high-performance \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Collected**} & \multicolumn{2}{c}{**Generated Dataset**} & \multicolumn{2}{c}{**Generated Dataset**} & \multicolumn{2}{c}{**Generated Dataset**} & \multicolumn{2}{c}{**Generated Dataset**} \\  & \multicolumn{2}{c}{**Original Dataset**} & \multicolumn{2}{c}{**Before Feature**} & \multicolumn{2}{c}{**with 5 Best Feature**} & \multicolumn{2}{c}{**with 10 Best Feature**} & \multicolumn{2}{c}{**with 15 Best Feature**} \\  & \multicolumn{2}{c}{**Selection**} & \multicolumn{2}{c}{**Selection**} & \multicolumn{2}{c}{**Selection**} & \multicolumn{2}{c}{**Selection**} & \multicolumn{2}{c}{**Selection**} \\ \hline
**Classifier** & **Train** & **Test Time** & **Train** & **Test Time** & **Train** & **Test Time** & **Train** & **Test Time** & **Train** & **Test Time** \\ \hline RF & 4.31 & 0.1 & 4.39 & 0.09 & 4.15 & 0.47 & 11.09 & 0.57 & 14.94 & 0.61 \\ Naive & 0.31 & 0.06 & 0.13 & 0.04 & 0.04 & 0.006 & 0.08 & 0.01 & 0.1 & 0.02 \\ Bayes & & & & & & & & & & \\ Decision & 1.48 & 0.02 & 2.14 & 0.02 & 0.05 & 0.004 & 0.51 & 0.007 & 0.91 & 0.008 \\ Tree & 94.14 & 0.09 & 51.91 & 0.09 & 24.70 & 0.07 & 31.95 & 0.08 & 26.0 & 0.09 \\ Gradient & 12.78 & 0.04 & 8.56 & 0.04 & 1.44 & 0.04 & 5.19 & 0.04 & 8.41 & 0.04 \\ XGBoost & 24.54 & 0.07 & 23.27 & 0.06 & 5.35 & 0.06 & 9.52 & 0.08 & 12.14 & 0.08 \\ LightGBM & 4.61 & 0.38 & 3.85 & 0.26 & 1.72 & 0.34 & 2.13 & 0.28 & 2.55 & 0.27 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Performance of training and test times in seconds with the 5, 10, and 15 best feature selections. Figure 6: Comparison with related studies that consider training and testing times, with the CTGAN-generated dataset using a decision tree classifier and the 5 best features [15; 20; 21]. results and lower training and testing times compared to the related studies presented in Table 1, even after using filter-based feature selection and reducing training and testing times. Moreover, compared with other published GAN network results, such as [31; 32], the proposed method can generate a dataset that is suitable for training ML models with higher accuracy across all three datasets. This is due to the universal features shared among the datasets, making it adaptable and not limited to only one type of dataset.