## 4 Results and Discussion
### Compilation and Comparison of Results  
Table 1 shows the accuracy results based on number of epochs. It can be observed that for the number of epochs less than 90, the classification accuracy increases when the size of features set increases from 5 to 20 features. However, when the size of features increase, the accuracy drops again. When the number of epochs increases to 120, the accuracy increases until the number of features reaches 25. After that, no significant increase was shown. If the number of epochs increased to 150, 180, and 210, the accuracy increases for the feature sets 10, 15, 20, and 35. When the number of features increases to 35 and above, the accuracy does not improve. Similarly, the accuracy when the feature set size is 5 fluctuates between 0.88 and 0.919. To show the efficacy of our proposed BGM-GAN techniques, the results were compared with the GAN-based detection model proposed by [36]. Furthermore, the comparison was conducted using the CNN and DBN models proposed by [37; 38]. Moreover, the experiments were conducted using different features set sizes ranging from 5 to 50 and incremented by 5 features between each of the two consequent sets. The classification accuracy was used as the measurement of the classification performance. Figure 3 shows the comparison results between our proposed BGM-GAN and the related techniques. Based on the comparison results, the proposed BGM-GAN outperforms GAN, CNN, and DBN. \begin{table}
\begin{tabular}{c c c c c c c c} \hline
**FeaturesEpochs** & **E30** & **E60** & **E90** & **E120** & **E150** & **E180** & **E210** \\ \hline
5 & 0.890 & 0.906 & 0.934 & 0.932 & 0.942 & 0.938 & 0.926 \\
10 & 0.908 & 0.920 & 0.951 & 0.935 & 0.954 & 0.953 & 0.957 \\
15 & 0.898 & 0.939 & 0.937 & 0.944 & 0.950 & 0.969 & 0.961 \\
20 & 0.915 & 0.959 & 0.956 & 0.954 & 0.956 & 0.965 & 0.962 \\
25 & 0.910 & 0.949 & 0.941 & 0.973 & 0.971 & 0.979 & 0.969 \\
30 & 0.927 & 0.960 & 0.931 & 0.963 & 0.980 & 0.967 & 0.967 \\
35 & 0.936 & 0.953 & 0.938 & 0.970 & 0.979 & 0.970 & 0.981 \\
40 & 0.937 & 0.925 & 0.953 & 0.971 & 0.976 & 0.991 & 0.986 \\
45 & 0.939 & 0.916 & 0.946 & 0.984 & 0.981 & 0.991 & 0.992 \\
50 & 0.938 & 0.933 & 0.922 & 0.989 & 0.984 & 0.994 & 0.995 \\ \hline \end{tabular}
\end{table} 
Table 1: Accuracy results of the proposed BGM-GAN based on number of epochs. Figure 3a shows an accuracy comparison of our proposed BGM-GAN with the related models (standard GAN, CNN, and DBN) across various feature counts, with each model trained for 30 epochs. Our proposed GAN demonstrates a general increase in accuracy as the feature count rises, peaking at 45 features with an accuracy of 0.939. There is a slight dip at 50 features (0.938), but the proposed GAN still displays consistent high performance. The standard GAN begins at a similar accuracy to our proposed GAN (0.891 at 5 features), with its highest accuracy achieved at 25 features (0.930). Post this peak, the accuracy diminishes slightly, reaching accuracy of 0.916 at 50 features. The CNN model starts with an accuracy of 0.865 at 5 features and reaches its peak accuracy at 30 features (0.929). Past this point, the model's accuracy regresses slightly, ending with an accuracy of 0.919 at 50 features. The DBN model, meanwhile, shows less fluctuation. It starts with 0.885 accuracy at 5 features and peaks at 20 and 30 features with an accuracy of 0.914. Thereafter, it maintains a relatively stable performance, ending with 0.906 at 50 features. In summary, all models exhibit peaks in performance at different feature counts. Our proposed BGM-GAN, however, consistently outperforms the others across the range of features, reaching the highest accuracy of all models at 45 features (0.939), as shown in Figure 3.  
Figure 3b shows the comparison of the accuracy of our proposed BGM-GAN and the related models, standard GAN, CNN, and DBN, when trained for 60 epochs across a varying number of features. The proposed GAN shows an increasing trend in accuracy as the number of features goes up to 30, peaking at an impressive 0.960 accuracy.