Harnessing Generative Adversarial Networks for The Proactive Prediction and Prevention of Zero Day Exploits in the Dynamic Infosec Ecosystem

A zero-day exploit is an online attack using a previously unknown software or hardware weakness before its vulnerabilities can be tackled by its creators. A zero-day exploit could result in unauthorized access, information loss, and extensive financial gains. Recent works have investigated utilizing Generative Adversarial Networks in predicting and detecting zero-day exploits. In the adversarial process, the discriminator and generator refine detection. Studies conducted between 2022 and 2024 have proved the capability of such models in detecting vulnerabilities. Nevertheless, existing GAN based methods are confronted by various challenges, such as high cost, scalability issues, unstable training, and slow responsiveness to newly emerging exploit attacks, rendering real-time detection of threats less efficient. This project proposes an improved GAN architecture tackling such problems through a hybrid approach merging Conditional or Wasserstein GANs and Reinforcement Learning, Vulnerability Intelligence, Graph Neural Networks, and Autoencoders. This stabilizes training, minimizes computation overhead, and maximizes flexibility to new exploit methods. Our model, with this architecture, will be able to provide more precise detection of zero-day exploits, strengthening cybersecurity defenses beyond existing techniques. This method is designed to be adaptive and scalable so that it can provide long-term security against evolving cyber threats.