Enhancing Connected Medical Device Security with Digital Twins and AI-Based Anomaly Detection

This blog post was written by ENTRUST partner SINTEF.

The growing integration of medical devices in healthcare has created a pressing need for advanced security mechanisms. Traditional cybersecurity approaches often struggle to keep pace with evolving threats, particularly in complex environments like the medical domain. To address these challenges in the ENTRUST project, SINTEF focuses on leveraging digital twins and AI-based anomaly detection to enhance the security of connected medical devices.

Digital Twins for Security Monitoring

A digital twin is a virtual representation of a physical system that continuously mirrors its real-world counterpart using live data. In cybersecurity, digital twins offer a powerful way to monitor device behavior, simulate attacks, and test mitigation strategies in a controlled environment without disrupting actual operations. Within ENTRUST, SINTEF designed a digital twin framework that integrates real-time monitoring, hardware emulation, and attack simulation to improve threat detection and response.

AI-Driven Anomaly Detection

One of the core capabilities of SINTEF's digital twin framework is unsupervised anomaly detection using AI models. Unlike traditional rule-based security mechanisms, the approach does not rely on predefined attack signatures. Instead, it identifies deviations from normal behavior patterns by analyzing system metrics such as CPU usage, memory consumption, disk I/O, network traffic, and control-flow integrity. This enables the detection of previously unseen threats, stealthy attacks, and suspicious behaviors that might evade conventional security solutions.

Towards a More Secure Future for Connected Medical Devices

By combining digital twins, AI-driven anomaly detection, and proactive threat modeling, our work contributes to next-generation cybersecurity solutions for medical devices. As we continue to develop and evaluate our approach, we aim to provide a scalable, efficient, and intelligent framework that strengthens the security of connected systems in real-world applications. Stay tuned for further updates as we refine our methods and expand our testing in more complex environments.

If you are interested in learning more about our research or collaborating on related topics, feel free to reach out or follow our progress in the ENTRUST project!