## 5 Conclusions  
The present study aimed to provide insights into the effectiveness of different GAN architectures for generating synthetic data that accurately represent malicious cyber-attacks. To compare the performance of different GAN models in generating synthetic data that accurately represent malicious cyber-attacks (botnet attacks), a well-known collection of network traffic captures widely utilized in cybersecurity research was used, namely the Stratosphere IPS CTU-13 dataset [23]. The dataset comprises thirteen captures of diverse malware samples and one capture of normal traffic obtained in a controlled environment. These captures, containing over 32 million packets, were amassed over several months and feature metadata labeling for each capture, such as the type of malware, timestamp and network protocols employed. The \"botnet\" captures are numbered from 1 to 5, and the \"normal\" captures are numbered from 11 to 15. These captures were taken at various stages of the botnet attack, with the initial capture taken shortly after activation and the subsequent captures taken at different intervals during the attack. The main objective of this study was to compare the performance of two distinct GAN architectures--one with six layers and the other with eight layers--for both the generator and discriminator, while varying the number of epochs from 25 to 1000. Thus, the aim of this study was to identify the model with the optimal performance and draw conclusions about the relationship between the considered parameters, including the number of epochs and GAN architectures (number of layers). According to the results, as presented in  
Figure 19: Similarity score chart for 6-layer and 8-layer architecture per number of epochs. Section 4, the best similarity score, between the real and generated data samples, was achieved for the suggested eight-layer GAN architecture, after the model was trained for 1000 epochs. Since the quality of the generated samples was the primary evaluation metric, the results indicated that the eight-layer GAN may perform better, as the additional layers provide more capacity for the model to learn complex patterns in the data. Moreover, it was observed that the number of epochs greatly affected the efficiency of the GAN model, based on the experimentation and evaluation of the model's performance using the selected metrics, for the similarity score calculation. In fact, increasing the number of epochs allowed the model to learn more complex patterns in the data and generate higher-quality synthetic samples. The field of cybersecurity is constantly evolving, and the use of GANs for data generation has become an important tool for detecting and preventing malicious cyber-attacks. These insights could be used for further evaluation of GAN architectures. The current study focused on two GAN architectures for generating synthetic botnet data. Future studies could investigate additional GAN architectures, such as progressive GANs, cycle GANs and attention GANs, to determine whether they can produce even more accurate and diverse synthetic botnet data. Moreover, the incorporation of real-world data could also be a great asset to future steps of the current study. While the study did manage to generate synthetic botnet data that accurately represented real-world botnets, incorporating real-world data into the training process could further improve the quality and diversity of these synthetic data. Thus, future studies could explore the feasibility of using real-world data to train GAN models for generating synthetic botnet data. In terms of the impact of data preprocessing on GAN performance, future studies and experiments could investigate different data preprocessing techniques, such as data normalization. Eventually, the adoption of hybrid models and methods that combine GANs with other machine learning techniques, such as autoencoders or deep neural networks, could be developed to generate even more accurate and diverse synthetic botnet data.