A Comparison Study of Generative Adversarial Network Architectures for Malicious Cyber-Attack Data Generation
Νικόλαος Πεππές, Theodoros Alexakis, Konstantinos P. Demestichas, Evgenia F. Adamopoulou · Applied Sciences · 2023
The digitization trend that prevails nowadays has led to increased vulnerabilities of tools and technologies of everyday life. One of the many different types of software vulnerabilities and attacks is botnets. Botnets enable attackers to gain remote control of the infected machines, often leading to disastrous consequences. Cybersecurity experts engage machine learning (ML) and deep learning (DL) technologies for designing and developing smart and proactive cybersecurity systems in order to tackle such infections. The development of such systems is, often, hindered by the lack of data that can be used to train them. Aiming to address this problem, this study proposes and describes a methodology for the generation of botnet-type data in tabular format. This methodology involves the design and development of two generative adversarial network (GAN) models, one with six layers and the other with eight layers, to identify the most efficient and reliable one in terms of the similarity of the generated data to the real ones. The two GAN models produce data in loops of 25, 50, 100, 250, 500 and 1000 epochs. The results are quite encouraging as, for both models, the similarity between the synthetic and the real data is around 80%. The eight-layer solution is slightly better as, after running for 1000 epochs, it achieved a similarity degree of 82%, outperforming the six-layer one, which achieved 77%. These results indicate that such solutions of data augmentation in the cybersecurity domain are feasible and reliable and can lead to new standards for developing and training trustworthy ML and DL solutions for detecting and tackling botnet attacks.