Securing Medical IoT Devices: AI-Based Approaches to Vulnerability Management
Keywords:
Medical IoT, Vulnerability Management, Artificial Intelligence, Machine Learning, Cybersecurity, Random Forest, Anomaly Detection, Healthcare Security.Abstract
Medical Internet of Things (IoT) devices are becoming a big part of modern healthcare because they let doctors keep an eye on patients and make decisions based on data. But their extensive use has revealed serious weaknesses that put patient safety and data privacy at risk. This study looked into using AI to manage vulnerabilities in medical IoT environments. We used real-world datasets and expert opinions to create and test machine learning models including Random Forest, Support Vector Machines, and Autoencoders to see how well they could find and categorize device vulnerabilities. The AI models were integrated into an automated vulnerability management framework, which demonstrated high detection accuracy, low false positive rates, and efficient response times within a simulated hospital network. Feedback from experts stressed the framework's usefulness in real life and the necessity for ongoing improvements to avoid alert fatigue. The findings confirm that AI-driven vulnerability management can significantly enhance the security posture of medical IoT devices, ensuring safer and more resilient healthcare delivery.
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