  With the proliferation of Artificial Intelligence, there has been a massive
increase in the amount of data required to be accumulated and disseminated
digitally. As the data are available online in digital landscapes with complex
and sophisticated infrastructures, it is crucial to implement various defense
mechanisms based on cybersecurity. Generative Adversarial Networks (GANs),
which are deep learning models, have emerged as powerful solutions for
addressing the constantly changing security issues. This survey studies the
significance of the deep learning model, precisely on GANs, in strengthening
cybersecurity defenses. Our survey aims to explore the various works completed
in GANs, such as Intrusion Detection Systems (IDS), Mobile and Network
Trespass, BotNet Detection, and Malware Detection. The focus is to examine how
GANs can be influential tools to strengthen cybersecurity defenses in these
domains. Further, the paper discusses the challenges and constraints of using
GANs in these areas and suggests future research directions. Overall, the paper
highlights the potential of GANs in enhancing cybersecurity measures and
addresses the need for further exploration in this field.
