A Systematic Study of Security Issues in Generative Adversarial Network Based Smart Energy Systems

Abstract

Generative Adversarial Networks (GANs) significantly enhance smart energy systems through demand forecasting and grid optimization; however, their integration exposes critical infrastructure to novel security threats that are inadequately addressed by traditional cybersecurity frameworks. While prior studies address generic machine learning vulnerabilities or energy system security, they overlook GAN-specific attack surfaces in hybrid AI-energy infrastructures. This study systematically analyzes security vulnerabilities in GAN-based energy systems to develop tailored countermeasures. We identify three core threats: adversarial attacks (evasion, model inversion), data poisoning, and system interface exploits, which demonstrate severe impacts, including financial losses from manipulated forecasts, grid instability, and privacy breaches. Empirical tests reveal GAN-generated attacks bypass industry-standard Web Application Firewalls in 8–44% of cases. We propose and validate a security framework integrating robust adversarial training, semantic tokenization for data sanitization, and 3D-Wasserstein GANs for anomaly detection. This work establishes foundational protocols for resilient AI-driven energy infrastructure, urging adoption in future deployments to safeguard against evolving cyber-physical threats.