Cross-Domain Reconfigurable GAN with Fuzzy Components for Anomaly Detection

Abstract

Anomaly detection remains a critical task in various domains, including cybersecurity and healthcare monitoring. Traditional approaches often rely on low-level machine learning and statistical methods, which may struggle to capture complex, multidimensional data patterns and adapt to evolving anomalies. In recent years, generative adversarial networks (GANs) have demonstrated promising potential for anomaly detection due to their ability to learn the underlying data distribution. This paper presents an anomaly detection system, which leverages a GAN-based model integrated with fuzzy logic components. We explore the integration of the GAN architecture with auxiliary components to enhance the performance and robustness of the anomaly detection system. This approach endeavors to explore the practical potential of GAN-based models in the field of anomaly detection and paves the way for future research in this rapidly evolving domain.