Data Guardians: Empowering Cybersecurity With Generative Adversarial Networks

Introduction

The advent of Generative Adversarial Networks (GANs) has sparked a transformative wave across machine learning and its associated domains, rapidly advancing a wide array of research areas and applications. This groundbreaking innovation has unleashed profound changes, marking a significant milestone in the evolution of Artificial Intelligence (AI) technologies.

The concept of GANs was first introduced in 2014 by Ian Goodfellow and his colleagues (Goodfellow et al., 2020; Zai et al., 2021). GANs are a class of AI algorithms used in unsupervised machine learning, implemented by a system of two neural networks contesting with each other in a game. This innovative approach involves two neural networks, the Generator (G) and the Discriminator (D), which are trained simultaneously through adversarial processes (Dutta et al., 2020; Pan et al., 2019). The Generator network creates new data instances, aiming to produce outputs that closely mimic real, authentic data. It operates by mapping input from a latent space to the desired data distribution. In contrast, the Discriminator network evaluates the authenticity of data, tasked with distinguishing between genuine data from the training set and counterfeit data produced by the Generator. Its goal is to accurately classify the inputs it receives as either “real” or “fake” (Figure 1). This adversarial process is designed to continuously improve the performance of both networks: the Generator is focused on improving the realism of its outputs, while the Discriminator aims to sharpen its ability to identify fake data (Dutta et al., 2020; Navidan et al., 2021). The competition between the two networks drives them to evolve until the Generator's data becomes so realistic that the Discriminator cannot differentiate it from real data.

Originally proposed to bridge the gap between supervised and unsupervised learning, GANs have been praised by Turing Award winner Yann LeCun as “the most interesting idea in the last 10 years in Machine Learning” (Zai et al., 2021). At their core, GANs use a generative model to learn from a training dataset drawn from a particular distribution, aiming to produce a new, estimated distribution that closely replicates the real one.

After their introduction, researchers made several refinements to GANs, aiming to enhance both their functionality and the quality of their outputs. Among these advancements, Conditional GANs have been a standout, allowing for the generation of data based on specific conditions or labels, leading to more controlled and diverse outputs. Additionally, the emergence of Deep Convolutional GANs (DCGANs) has significantly improved the quality of the images produced, thanks to the broader incorporation of deep convolutional neural networks in their architecture (Bian et al., 2019; Viola et al., 2021). The introduction of Progressive Growing GANs (ProGAN) further advanced image quality by progressively increasing the resolution of generated images, a technique that enabled the creation of highly detailed and realistic images (Speck et al., 2023; Song et al., 2021).