  This paper investigates the feasibility and effectiveness of employing
Generative Adversarial Networks (GANs) for the generation of decoy
configurations in the field of cyber defense. The utilization of honeypots has
been extensively studied in the past; however, selecting appropriate decoy
configurations for a given cyber scenario (and subsequently
retrieving/generating them) remain open challenges. Existing approaches often
rely on maintaining lists of configurations or storing collections of
pre-configured images, lacking adaptability and efficiency. In this pioneering
study, we present a novel approach that leverages GANs' learning capabilities
to tackle these challenges. To the best of our knowledge, no prior attempts
have been made to utilize GANs specifically for generating decoy
configurations. Our research aims to address this gap and provide cyber
defenders with a powerful tool to bolster their network defenses.
