## 5 Comparison  
The proposed study uses the UNSW-NB15 dataset by applying binary and multi-class classification as the original and oversampled datasets. Therefore, we compared it with binary and multi-class studies that used original or oversampled datasets. The comparative summary is shown in Table 7.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Study** & **Year** & **Methods** & **Dataset** & **Results** \\ \hline
[29] & 2018 & Two-stage approach for network intruder detection using SMOTE & UNSW-NB15 & Multi-class classification Accuracy = 85.78\% \\ \hline
[25] & 2020 & Focal-loss-based DNN and CNN & UNSW-NB15 & DNN Binary-F1 = 90.41\%, Multi-F1 = 39.78\%, CNN \\  & & & Binary-F1 = 86.03\%, Multi-F1 = 39.52\% \\ \hline
[30] & 2021 & Image-based network intrusion detection and DL-based classification & UNSW-NB15 & ML-Net Binary Micro-Accuracy = 92.87\% Multi-class Micro-Accuracy = 72.31\% \\ \hline
[26] & 2022 & Modified cross entropy applied for class imbalance and neural network applied for classification & UNSW-NB15 & Accuracy = 90.76\% \\ \hline Proposed study & 2022 & GAN-based class balancing and chi-square feature selection-based ML classification & UNSW-NB15 & Binary Accuracy = 98.14\% Precision = 98.14\% F1-score = 98.14\% G-Mean = 0.976, AUC = 0.976 \\ \hline Proposed study & 2022 & GAN based class balancing and chi-square feature selection-based ML classification & UNSW-NB15 & Multi-class Accuracy = 87.44\% Precision = 87.81\% F1-score = 86.79\% G-Mean = 0.923, AUC = 0.94 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Comparative analysis of UNSW-NB15 dataset with respect to recently applied state-of-the-art studies.  
Figure 6: Confusion matrices of multi-class classification on original + GAN-based new dataset (second 3 classifiers).  
The first comparison [29] includes SMOTE in its proposed two-step approach for network intruder detection and showed that the multi-class 10 category classification approach reached up to 85.78%. The second comparison [25] study was discussed in the related work section in detail, where the achieved results by this study showed a maximum of 90.41% binary class F1-score and 39.78% F1-score on multi-class classification using the DNN method. The third study [30] used the image-conversion-based approach using the DL approach to classification on UNSW-NB15 and other datasets, where oversampling is used to increase the instances. The applied methods achieved the highest macro accuracies: 92.87%, in binary and, 72.31%, multi-class classification.  
The fourth comparison used only the binary class to classify and achieved 90.76% accuracy by solving the class unbalancing problem using the modified cross entropy function. We compared here both binary and multi-class classification samples to increase the validity of our proposed framework. However, by looking at comparative studies, some of them proposed class imbalance problem solutions and some applied oversampling techniques; none reached the proposed framework results of binary and multi-class results regarding accuracy, precision, or F1-score metrics.It proved the outperformance of the proposed study on the recently applied state-of-the-art approaches.