Improving security for connected medical devices using artificial intelligence
Official title: An Explainable Artificial Intelligence Based Hybrid Intrusion Detection System for Enhancing Healthcare Security
Funded at Keene State College · NIH-11043087 · Data updated 2026-11-12
This study is working on a new way to keep medical devices safe from cyber threats by using smart technology that can explain its decisions, helping healthcare providers protect patient information and ensure safety.
Quick facts
| Grant type | R15 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | Keene State College NIH-funded |
| Lab location | 1 site (Keene, United States) |
| Project start | September 20, 2024 |
| Project end | September 19, 2027 |
| Funding year | 2024 |
| Award (this year) | $357,285 |
| NIH project number | 1R15EB036778-01 |
| Project ID | NIH-11043087 on NIH RePORTER |
Who could benefit from this research
✓Could help
Ideal candidates for this research are patients who use or are treated with IoMT devices, such as implantable cardioverter defibrillators or other connected medical devices.
✕Less likely to help
Patients who do not use any connected medical devices or who are not involved in healthcare settings may not receive direct benefits from this research.
What this research studies
This research focuses on enhancing the security of medical devices connected through the Internet of Medical Things (IoMT) by developing a hybrid intrusion detection system that utilizes explainable artificial intelligence (XAI). It aims to address the significant security and privacy concerns that arise from the increasing use of IoMT in healthcare settings. By integrating machine learning algorithms, the project seeks to predict and identify potential cyber threats while providing understandable explanations of its decisions to healthcare administrators. This approach is designed to ensure patient safety and improve the overall security framework for healthcare providers.
Why it matters
Potential benefit: If successful, this research could significantly enhance the security of medical devices, thereby protecting patient data and ensuring safer healthcare delivery.
How similar studies have gone: While the integration of AI in healthcare security is a growing field, this specific approach using explainable AI for intrusion detection in IoMT is relatively novel and has not been widely tested.
Where this research is happening
-
Keene State College
Keene, United States Active
Researchers
- Principal investigator: Lu, Wei — Keene State College
- Study coordinator: Lu, Wei
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About this research
- This is an active NIH-funded research project — typically early-stage science, not a clinical trial accepting patient enrollment.
- Some NIH-funded labs run parallel clinical studies or seek volunteers for related work. To check, contact the principal investigator or institution listed above.
- For full project details, budget, and progress reports, visit the official NIH RePORTER page.