## 4 Results and Discussion
### Experiment 2: GAN-Based Dataset  
The second dataset contains the GAN-based instances only for attacked categories and the normal instances taken from the original dataset. Then the contingency of new instances is proved, and the results are reported for binary and multi-class, as shown in Table 5.  
Table 5 explores the score achieved by the six classifiers on GAN-based created instances and the normal class instances taken from the original dataset. However, the performance with these generated instances is improved as compared to the original ones. There is no significant difference, but it is improved, as we can see that for binary classification, the random forest again showed the highest accuracy of 95.41%, recall was the same, and the precision and F1-score were also highest with 95.44% and 95.42% with 0.954 G-Mean, AUC scores. The multi-class classification also showed the highest accuracy of 84.53% against random forest classifiers, and the nearer or second score classifier is the same.  
However, in the binary class results, the results this time go down against logistic regression, where the performance of other classifiers is improved. Further, the multi-class classification results were the worst in the case of the original dataset against logistic regression, and in this experiment, worst with 68.62% accuracy. Now let us check again the time efficiency and the training and testing times of experiment 2.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Classification** & **Method** & **Accuracy** & **Recall** & **Precision** & **F1-Score** & **G-Mean** & **AUC** &
\begin{tabular}{c} **Time(sec) Train +** \\ **Test = Total** \\ \end{tabular} \\ \hline Binary & MLP & 92.61\% & 92.61\% & 92.74\% & 92.64\% & 0.928 & 0.928 & 126.6 + 0.1 = 126.7 \\ \hline KNN & 93.36\% & 93.36\% & 93.36\% & 93.36\% & 0.928 & 0.928 & 0.1 + 13.4 = 13.5 \\ \hline Logistic Regression & 93.36\% & 93.36\% & 93.36\% & 93.36\% & 0.84 & 0.85 & 0.1 + 13.4 = 13.5 \\ \hline Decision Tree & 93.97\% & 93.97\% & 93.97\% & 93.97\% & 0.935 & 0.935 & 3.2 + 0.0 = 3.2 \\ \hline Random Forest & 95.59\% & 95.59\% & 95.60\% & 95.60\% & 0.953 & 0.953 & 8.8 + 0.2 = 9.0 \\ \hline Extra Trees & 95.35\% & 95.35\% & 95.36\% & 95.35\% & 0.950 & 0.950 & 7.0 + 0.2 = 7.2 \\ \hline Multi-class & MLP & 78.63\% & 78.63\% & 75.73\% & 75.36\% & 0.86 & 0.91 & 149.2 + 0.1 = 149.3 \\ \hline KNN & 78.41\% & 78.41\% & 79.40\% & 78.82\% & 0.87 & 0.78 & 0.1 + 14.7 = 14.8 \\ \hline Logistic Regression & 65.47\% & 65.47\% & 61.53\% & 62.59\% & 0.77 & 0.78 & 15.2 + 0.0 = 15.2 \\ \hline Decision Tree & 81.38\% & 81.38\% & 81.18\% & 80.90\% & 0.88 & 0.80 & 5.3 + 0.0 = 5.4 \\ \hline Random Forest & 83.36\% & 83.36\% & 83.31\% & 82.35\% & 0.90 & 0.89 & 10.9 + 0.6 = 11.5 \\ \hline Extra Trees & 83.16\% & 83.16\% & 82.91\% & 82.35\% & 0.89 & 0.88 & 7.7 + 0.6 = 8.3 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Classification results on UNSW-NB15 dataset using chi-square features selection.  
The time efficiency could be seen such that in a few classifiers, training took time, and in a few, testing took more time. However, as proven previously, MLP took more total time as compared to other classifiers. The best classifiers are still the same here: random forest and extra trees took 8 and 5.9 seconds, respectively, in binary classification. However, in multi-class classification again the MLP is the worst, and the best ones took 12.3 and 9.6 total time. The training and testing times were greater as compared to the Experiment 1 in terms of multi-class classification, where in binary classification, Experiment 2 took less time.