## 4 Results and Discussion
### Experiment 1: Original UNSW-NB15 Dataset  
The proposed study first applied features selection using the chi-square method and then fed the16 selected features for binary and multi-class classification separately. The six classifiers were employed with an 80:20 holdout validation split. The training-data-based model was then tested, where the training and tested times were also estimated to obtain the time efficiency for all of the models' performance results. The accuracy, precision, recall, and F1-score were calculated. The detailed results are shown in Table 4.  
In Table 4, the classifier-wise performance of multi-class and binary-class data is shown. As we can see, different classifiers showed different behavior on both types of classification. If we look at binary classification, we can see that the best results in terms of accuracy are achieved by random forest classifier (95.59% accuracy), where the recall, precision, F1-score, geometric mean (G-mean) and area under cover (AUC) for this model are also greater than all of the other classifiers having 95.59%, 95.60%, 95.60%, 0.953 and 0.953 scores, respectively. The extra trees also achieved the best scores but here, we can say it value remains at second place among all classifiers. The other classifiers showed scores greater than 90% in all reported metrics.  
If we look at the multi-class classification results, random forest showed the best results with 83.36% accuracy, 83.36% recall, 83.31% precision, 82.35% F1-score, 0.90 G-mean  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Serial Number** & **Parameter** & **Value** \\ \hline
1 & Generator time & 1.1 \\ \hline
2 & Bot filter quantile & 0.0001 \\ \hline
3 & Top filter quantile & 0.99 \\ \hline
4 & Loss & RMSE \\ \hline
5 & Maximum depth & 2 \\ \hline
6 & Maximum bin & 100 \\ \hline
7 & Learning rate & 0.001 \\ \hline
8 & Random state & Yes \\ \hline
9 & Estimators & 100 \\ \hline
10 & Batch size & 500 \\ \hline
11 & Patience & 25 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Hyperparameters and their values used in GAN for data generation.  
and 0.89 AUC. The second best is still extra trees with 83.16% accuracy and recall. If we look at logistic regression that is basically used as a binomial classifier, it showed appropriate results in the binary classifier but failed in multi class classification, as it showed 65.47% accuracy. However, we need to see the performance of classifiers in terms of time efficiency as well in the table. The time taken to train and to predict could be another metric to measure the performance of a classifier. However, DL took more time to train, as we can see in Table 4 that [PERSON] took the highest time to train among all classifiers in both binary and multi-class classification at more than two minutes. The other classifiers, KNN and logistic regression, took less time in training but took more time in testing or prediction. The best performing classifiers random forest, and extra trees, showed 9 and 7.2 seconds in total for binary classification, where in multi-class classification, they took 11.5 seconds and 8.3 seconds in total. Therefore, as compared to classifiers other than the decision tree, the performed classifiers are also time-efficient for both binary and multi-class classification.