Application of Generative Adversarial Networks (GANs) for Generating Synthetic Data and in Cybersecurity

Abstract

GANs are one of the most recent innovations in artificial intelligence (AI) that have improved the field. The bulk of data saved and transported in the world of information technology is digital. Companies utilise data to enhance customer experience and give better services to their consumers. Data collecting can be time-consuming and expensive at times. GANs, specifically Conditional GAN, are a method for synthetic data production and how they may be used to produce synthetic datasets from them. When it comes to cybersecurity, machine learning is an extremely important technology, providing for advanced detection and protection techniques for safeguarding our data. The main topic is the Generative Adversarial Network (GAN), which is a very powerful machine learning paradigm. A GAN's applications in cyber security are not limited to data generation; the GAN may also evade detection systems. This may be used to create malware that avoids detection by machine learning-based systems. We will talk about the new issues that GANs have created for intrusion detection systems. Given the positive findings obtained in many GAN applications, it is extremely plausible that GANs may influence security improvements when used to cybersecurity. GAN's game theoretic optimization technique not only gives excellent performance on data generation-based problems, but it also encourages fertilisation for privacy and security-oriented research. Unfortunately, no extensive assessments on GAN in privacy and security exist.

Keywords: AI, GANs, Synthetic data, Cybersecurity, Privacy

JEL Classification: Z39