AI-Enabled Secure Monitoring of Computer Vision Data in 5G IoT-Based Healthcare Systems
Authors:- Research Scholar Ms. Komal Garg, Professor Dr. Narender Kumar
Abstract:- The integration of 5G-enabled IoT networks has significantly transformed the healthcare sector, enabling real-time monitoring, data collection, and advanced diagnostics through the Internet of Medical Things (IoMT). This study investigates the application of artificial intelligence (AI) models for securing sensitive patient data in IoMT networks, addressing the escalating threats of cyberattacks and privacy violations. By leveraging historical datasets, the research develops an AI-driven framework for anomaly detection and threat classification, optimizing data security through federated learning, encryption, and adaptive defences. The results demonstrate the model’s exceptional performance, with accuracy rates exceeding 99% across various threat scenarios and robust anomaly detection capabilities validated through real-world simulations. Additionally, the framework achieves a high AUC score of 0.999, showcasing its reliability and precision in securing IoMT networks. These findings underscore the efficacy of the proposed approach in mitigating security risks, enhancing system reliability, and ensuring the seamless operation of smart healthcare systems. This work highlights the critical role of 5G and AI technologies in creating robust, secure, and scalable IoMT ecosystems, contributing to improved healthcare accessibility and patient outcomes.
