## 4 Results and Discussion
### Experiment 3: Original UINSW-NB15 + GAN Dataset  
The original and GAN-based generated instances from attacked categories were combined to give a new dataset with normal class data of original data. To check the validity upon solving the class imbalance ratio to some extent, the proposed study-based dataset was fed to the same six ML classifiers, and the results for binary and multi-class were calculated. The results are shown in Table 6.  
Table 6 first shows the binary and then the multi-class classification results. The classification results reported for logistic regression were the worst results among all other classifiers, as was also the case in previous experiments. However, the binary classification on this new dataset achieved 98.14% highest accuracy using the extra trees methods after applying the chi-square method of feature selection. In Experiments 1 and 2, we see that random forest remains the highest score achiever for both binary and multi-class classification. However, in Experiment 3, random forest is second with respect to the six classifiers of the binary category. If we look at recall, precision and F1-scores, then extra trees is also first with 98.14% recall, precision, F1-score, and 0.976 G-mean and AUC scores. Further, all classifiers remain greater than 95% except logistic regression which is less than or nearer to 90%. The confusion-matrix-based instance-based true positive, false positive, true negative, and false negative are also shown in Figure 4.  
If we look in the MLP confusion matrix, it shows that in the 0 class, there are 23,064 predictions which correctly predicted where 2996 were predicted to be wrong and 56,568 were predicted as right in Class 1, where only 1190 were wrongly predicted. This was an MLP classifier only that did not perform as the best classifier among all. The best performing, random forest and extra trees need to be discussed here. Random forest predicted 1146 as being wrong predictions from Class 0, and 490 wrongly predicted from Class 1. \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 & 91.61\% & 91.61\% & 91.78\% & 91.55\% & 0.923 & 0.92 & 70.0 + 0.0 = 70.0 \\ \hline  & KNN & 93.03\% & 93.03\% & 93.05\% & 93.04\% & 0.930 & 0.929 & 0.1 + 11.2 = 11.3 \\ \hline  & Logistic Regression & 86.56\% & 86.56\% & 87.12\% & 86.34\% & 0.847 & 0.852 & 2.9 + 0.0 = 3.0 \\ \hline  & Decision Tree & 93.80\% & 93.80\% & 93.80\% & 93.80\% & 0.937 & 0.937 & 2.5 + 0.0 = 2.5 \\ \hline  & Random Forest & 95.41\% & 95.41\% & 95.44\% & 95.42\% & 0.954 & 0.954 & 7.9 + 0.2 = 8.0 \\ \hline  & Extra Trees & 95.25\% & 95.25\% & 95.27\% & 95.25\% & 0.953 & 0.952 & 5.7 + 0.2 = 5.9 \\ \hline  & Extra Trees & 95.25\% & 95.25\% & 95.27\% & 95.25\% & 0.953 & 0.952 & 5.7 + 0.2 = 5.9 \\ \hline  & Kntr & 81.02\% & 81.02\% & 79.29\% & 77.87\% & 0.87 & 0.91 & 123.2 + 0.1 = 123.3 \\ \hline  & KNN & 79.95\% & 79.95\% & 80.62\% & 80.21\% & 0.87 & 0.766 & 0.1 + 15.1 = 15.3 \\ \hline  & Logistic Regression & 68.62\% & 68.62\% & 62.22\% & 64.27\% & 0.77 & 0.78 & 12.1 + 0.0 = 12.2 \\ \hline  & Decision Tree & 82.39\% & 82.39\% & 82.14\% & 82.04\% & 0.888 & 0.807 & 9.6 + 0.0 = 9.6 \\ \hline  & Random Forest & 84.53\% & 84.53\% & 83.84\% & 83.58\% & 0.90 & 0.88 & 11.8 + 0.5 = 12.3 \\ \hline  & Extra Trees & 84.36\% & 84.36\% & 83.70\% & 83.59\% & 0.90 & 0.88 & 9.0 + 0.5 = 9.6 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Classification results on GAN dataset using chi-square features selection. The wrong predictions did not improve much as compared to MLP for Class 0, but for Class 1, they were reduced to 490. Extra trees reduced the 0 and 1 Classes' wrong predictions, as 983 were predicted to be wrong for Class 0 and 574 for Class 1. The Class 1 predictions increased here as compared to the Random forest classifier but the Class 0 wrong predictions were also reduced.