Improving security for connected medical devices using artificial intelligence
An Explainable Artificial Intelligence Based Hybrid Intrusion Detection System for Enhancing Healthcare Security
['FUNDING_R15'] · KEENE STATE COLLEGE · NIH-11043087
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
| Phase | ['FUNDING_R15'] |
|---|---|
| Study type | Nih_funding |
| Sex | All |
| Sponsor | KEENE STATE COLLEGE (nih funded) |
| Locations | 1 site (KEENE, UNITED STATES) |
| Trial ID | NIH-11043087 on ClinicalTrials.gov |
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.
Who could benefit from this research
Good fit: 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.
Not a fit: 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.
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 performed: 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, UNITED STATES
- KEENE STATE COLLEGE — KEENE, UNITED STATES (ACTIVE)
Researchers
- Principal investigator: LU, WEI — KEENE STATE COLLEGE
- Study coordinator: LU, WEI
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 below.