# A Deep Learning Framework for Enhanced Detection of Polymorphic Ransomware
## 4 Experimental Evaluation and Analysis
### Compilation and comparison of results  
To demonstrate the efficacy of our BGM-GAN technique, we compared its performance with GAN [67], CNN [68], and DBN [69] models. Feature set sizes ranging from 5 to 50, in increments of 5, were evaluated experimentally. Table 3 shows the accuracy results based on number of epochs. Figures 4-7 illustrate the classification accuracy results, demonstrating that BGM-GAN outperforms the other models. Figure 4 compares the performance of our BGM-GAN against standard GAN, Convolutional Neural Network (CNN), and DBN models, trained for 30 epochs across varying feature counts. BGM-GAN demonstrates a consistent improvement in accuracy with increasing features, peaking at 0.939 with 45 features and maintaining high performance even with a minor drop to 0.938 at 50 features. The standard GAN model reaches a maximum accuracy of 0.930 at 25 features, declining from 0.891 at 5 features to 0.916 at 50 features. The CNN model peaks at 0.929 with 30 features, starting at 0.865 and ending at 0.919. The DBN model shows the least variability, peaking at 0.914 with 20 and 30 features, and ending at 0.906. Overall, BGM-GAN consistently outperforms the other models, reaching the highest accuracy of 0.939 at 45 features, as shown in Figures 4-7. Figure 5 compares the performance of BGM-GAN against standard GAN, CNN, and DBN models, all trained for 90 epochs and evaluated across varying feature counts. BGM-GAN demonstrates strong initial accuracy, starting at 0.934 with five features and peaking at 0.951 with ten features. Though accuracy dips to 0.931 at thirty features, it rebounds, reaching a second peak of 0.953 at forty features before a slight decline to 0.922 at fifty features. In contrast, the standard GAN model peaks at 0.927 with thirty features, starting at 0.882 and stabilizing at 0.927 at fifty features. The CNN model reaches a peak of 0.924 at forty features, starting at 0.874 and ending at 0.916. The DBN model peaks at 0.932 with thirty features, starting at 0.884 and ending at 0.916. Despite feature-dependent variations in accuracy across all models, BGM-GAN consistently outperforms the others, achieving peak accuracies of 0.951 and 0.953 at ten and forty features, respectively. Figure 6 compares the accuracy of BGM-GAN, standard GAN, CNN, and DBN models, all trained for 150 epochs, across varying feature counts. BGM-GAN demonstrates a steady increase in accuracy, starting at 0.942 with five features and peaking at 0.984 with fifty. Though it experiences a slight dip to 0.950 at fifteen features and 0.976 at forty, BGM-GAN consistently outperforms the other models. In contrast, the standard GAN model peaks at 0.961 with twenty-five features, declining to 0.945 at fifty, with a low of 0.879 at fifteen. The CNN model peaks at 0.949 with forty-five features, ending at 0.899. The DBN model's performance fluctuates, peaking at 0.944 with thirty-five features and ending at 0.930. BGM-GAN's consistent upward trend and peak accuracy of 0.984 highlight its superiority. Figure 7 compares the performance of BGM-GAN, standard GAN, CNN, and DBN models, all trained for 210 epochs, across varying feature counts. BGM-GAN demonstrates a consistent upward trend, starting at 0.926 with five features and peaking at 0.995 with fifty, highlighting its robustness. In contrast, standard GAN fluctuates, peaking at 0.979 with thirty features and ending at 0.977. The CNN model peaks at 0.959 with twenty-five features, and the DBN model at 0.950, also with twenty-five features. BGM-GAN consistently outperforms the others, achieving the highest accuracy of 0.995. Figures 4-7 demonstrate that our BGM-GAN model consistently achieved superior detection accuracy compared to the GAN model presented in [67] across all training epochs. Furthermore, BGM-GAN outperformed other deep learning models, including CNN and DBN.