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DIMScern: A Framework for Discerning DIMSE Services on Remote Medical Devices
Gunhee Kim1, Dohyun Kim2, Jeonghun Seo3
1Department of Convergence Security, Kangwon National University, 1 Kangwondaehak-gil, Chuncheon-si 24341, Republic of Korea.
Sensors (Basel, Switzerland)
|December 17, 2024
Summary
This study introduces DIMScern, a framework to identify Digital Imaging and Communications in Medicine Message Service Element (DIMSE) services on remote medical devices. It addresses security concerns by revealing available services on healthcare IT systems.
Area of Science:
- Digital Health and Medical Informatics
- Cybersecurity in Healthcare
- Network Protocol Analysis
Background:
- The DICOM Message Service Element (DIMSE) protocol is crucial for exchanging sensitive medical imaging data.
- Increased connectivity in digital healthcare makes DIMSE a target for security attacks.
- DIMSE implementations vary across products, leading to inconsistent service availability.
Purpose of the Study:
- To investigate security concerns related to DIMSE message exchange methods.
- To develop a framework for identifying available DIMSE services on remote medical devices.
- To assess the variability of DIMSE service implementations in healthcare IT systems.
Main Methods:
- Development of a novel framework named DIMScern.
- Evaluation of DIMScern on various DIMSE implementations, including commercial products and libraries.
- Remote identification of supported DIMSE services across different healthcare IT systems.
Main Results:
- DIMScern successfully identifies available DIMSE services on remote medical devices.
- The framework demonstrated effectiveness across multiple DIMSE implementations.
- Significant variations in supported DIMSE services were identified across 22 healthcare IT systems.
Conclusions:
- DIMScern provides a valuable tool for understanding the security landscape of DIMSE services in healthcare.
- Awareness of available DIMSE services is critical for identifying potential security vulnerabilities.
- The framework aids in assessing the security posture of digital healthcare systems by highlighting service inconsistencies.
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