## 3 Material and Methods
### Six-Layer Architecture  
Figures 7-12 depict the selected diagrams, as previously described, for the starting epochs (25) and the final number of epochs (1000) as defined in Section 3.  
Figure 7: Correlation matrix for 6 layers, 25 epochs.  
Figure 8: Cumulative sums per feature for 6 layers, 25 epochs.  
Figure 9: Absolute log mean and standard deviation for 6 layers, 25 epochs.  
indicating that the GAN model may need more training epochs to produce a synthetic dataset that would be closer to the real one.  
Moreover, the correlation matrix diagrams for the real dataset indicate a strong positive correlation between the variables included. However, in the generated synthetic dataset, there was a weaker positive correlation between the variables, and no significant negative correlation was detected. In addition, a strong positive correlation was observed between the different features of the real and generated data features in the \"Difference\" section, indicating that the generated data replicated the patterns and characteristics presented in the real dataset in a realistic way, as the epochs increased.  
Finally, through the absolute mean and standard deviation diagrams, it was observed that the synthetic dataset includes higher values for some of the features compared to the real dataset. This indicates that the generated data for some features may not have been as close to the real data as for other features. As the number of training epochs increased, the generated dataset became more similar to the real dataset.  
In Appendix A, the same diagrams for  
Figure 14: Cumulative sums per feature for 8 layers, 25 epochs.  
Figure 15: Absolute log mean and standard deviation for 8 layers, 25 epochs.  
It is obvious again that for five of the eight variables (Dur, TotPkts, TotBytes, SrcBytes and Label), the cumulative sum diagrams show a steady increase in the similarity score both for the real and the generated datasets, indicating a consistent and continuous pattern of data. On the other hand, for three of the eight variables (Sport, Dport and State), a fluctuating pattern with sudden spikes and drops in the similarity score was also observed. In the same way as before, this fact states that the synthetic dataset generated by the GAN model contains certain data points that deviate significantly from the overall pattern, leading to a lower overall similarity score for the specific data features. Furthermore, for a small number of epochs, the cumulative sum diagrams again show a slower increase in the similarity score compared to the real dataset, indicating that the GAN model may need more training epochs to produce a synthetic dataset that would be closer to the real one.  
Figure 16: Correlation matrix for 8 layers, 1000 epochs.  
Figure 17: Cumulative sums per feature for 8 layers, 1000 epochs.  
To this end, as the number of epochs increased, the generated dataset would show higher similarity compared to the real one.  
Moreover, in the correlation matrix diagrams, a positive correlation was observed between the different features of the real and generated data features in the \"Difference\" section, indicating that the generated data replicated the patterns and characteristics presented in the real dataset in a realistic way, as the epochs gradually increased.  
Eventually, in the absolute mean and standard deviation diagrams, as was expected, with the increase in the number of epochs, the generated dataset became more similar to the real dataset, since fewer higher values are observed for some of the generated data features compared to the real ones.  
In Appendix B, the same diagrams for 50, 100, 250 and 500 epochs are also included.