AI-Driven Adversarial Defense Framework with Generative Adversarial Network for Secure Healthcare IoT Ecosystems

Abstract

This study developed and assessed an AI-driven adversarial defense framework using Generative Adversarial Networks (GANs) to secure healthcare IoT ecosystems against rising cybersecurity threats in medical settings. The research drew on datasets (CICIoMT2024, WUSTL-EHMS-2020, BoT-IoT, and Kaggle) and peer-reviewed studies to achieve three objectives: building a detailed threat model, designing a GAN-based defense optimized for healthcare IoT, and rigorously testing its effectiveness. The threat model revealed 127 vulnerability vectors, with adversarial attacks (32%) most prevalent, and a mean risk score of 7.82, highest for critical care devices (9.34). The GAN framework, featuring a multi-layer generator–discriminator pair and 128-dimensional encoder, achieved a mean accuracy of 95.8% against major adversarial attacks (FGSM 97.1%, PGD 94.8%, C&W 95.9%, UAP 96.4%), outperforming traditional defenses by 39.4%. With an MTTD of 82 ms, the system enables real-time deployment, allowing healthcare providers to integrate it directly into hospital IoT networks for proactive protection. Limitations include reliance on secondary data and high computational cost. Recommendations include hybrid datasets, explainable AI integration, real-world pilots, standardized metrics, and federated learning to enhance scalability and adaptability.