## 4 Results and Discussion
### Experiment 3: Original UINSW-NB15 + GAN Dataset
Therefore, in this way, the extra trees showed a slightly higher score in binary classification as compared to the random forest method. \begin{table}
\begin{tabular}{c 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 & 95.00\% & 95.00\% & 95.00\% & 94.95\% & 0.920 & 0.922 & 216.2 + 0.1 = 216.3 \\ \hline  & KNN & 95.84\% & 95.84\% & 95.83\% & 95.84\% & 0.950 & 0.949 & 0.4 + 60.0 = 60.4 \\ \hline  & Logistic Regression & 89.39\% & 89.39\% & 89.35\% & 89.14\% & 0.849 & 0.855 & 8.2 + 0.0 = 8.2 \\ \hline  & Decision Tree & 97.68\% & 97.68\% & 97.68\% & 97.68\% & 0.971 & 0.972 & 9.6 + 0.1 = 9.7 \\ \hline  & Random Forest & 98.05\% & 98.05\% & 98.05\% & 98.04\% & 0.974 & 0.973 & 30.0 + 0.5 = 30.5 \\ \hline  & Extra Trees & 98.14\% & 98.14\% & 98.14\% & 98.14\% & 0.976 & 0.976 & 23.0 + 1.2 = 24.2 \\ \hline  & MLP & 80.25\% & 80.25\% & 78.86\% & 77.50\% & 0.88 & 0.92 & 335.5 + 0.1 = 335.6 \\ \hline  & KNN & 80.69\% & 80.69\% & 81.81\% & 81.20\% & 0.88 & 0.83 & 0.2 + 35.3 = 35.5 \\ \hline  & Logistic Regression & 67.38\% & 67.38\% & 64.50\% & 64.64\% & 0.78 & 0.74 & 22.9 + 0.0 = 22.9 \\ \hline  & Decision Tree & 86.80\% & 86.80\% & 87.04\% & 86.24\% & 0.919 & 0.90 & 6.4 + 0.1 = 6.5 \\ \hline  & Random Forest & 87.38\% & 87.38\% & 87.84\% & 86.67\% & 0.923 & 0.939 & 20.2 + 1.0 = 21.2 \\ \hline  & Extra Trees & 87.44\% & 87.44\% & 87.81\% & 86.79\% & 0.923 & 0.94 & 15.7 + 1.0 = 16.7 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Classification results on GAN + original dataset using chi-square feature selection. Figure 4: Confusion matrices of binary classification on original + GAN-based new dataset. Now, if we look at multi-class classification, the Extra trees is again the highest score achievement, where 87.44% recall, 87.81% precision, 86.79% F1-score, 0.923 G-mean, and 0.94 AUC scores are achieved. The Second place was taken by random forest again in binary classification with scores of 87.38% accuracy, and recall, 87.84% precision, 86.67% F1-score, 0.923 G-mean and roundly 0.94 AUC. Logistic regression again failed here to classify the multi-class instances, with 67.38% accuracy and recall, 64.50% precision, 64.64% F1-score, 0.78 G-mean, and 0.74 AUC. However, MLP and KNN remain above 80%, where three others showed more than 86% results. The results showed improvement compared to Experiments 1 and 2 for both binary and multi-class classification. It shows that the class imbalance not only reduces the classification accuracy, but also compromises to achieve a score with a greater number of samples as a normal category in it. The confusion matrices are shown in Figures 5 and 6 regarding multi-class classification to check each classifier's performance by instance. The confusion matrices of random forest and extra trees need to be discussed here as the best performers. The behavior of predictions in random forest remains good for all classes, but those for 3, 4, 5, 6, and 9, are better compared to other classes. The correctly predicted instances are 3866, 7207, 20,000, 4062, and 24,915 for the 3, 4, 5, 6 and 9 class numbers, respectively. These numbers of rightly predicted instances are greater than those of other classes. These class are DoS, exploits, fuzzers, generic and normal classes, where other classes are predicted to a good extent as well. To compare Extra trees with it, we will again look at these predicted instances. In the case of extra trees, these numbers remain at 3779, 7114, 20,000, 4046, and 25,044 for DoS, exploits, fuzzers, generic, and normal classes. Although, as compared to the random forest results of these five classes, the number of predictions is less for DoS, exploits, and generic classes, where normal class instances are higher as compared to random forest.