The Healthcare Cyber Range Project
The HCCR project is building a digital training environment for healthcare and developing the cybersecurity skills and processess of healthcare actors. The project's partners are HUS , HVK, KELA, KSSHP, Traficom / NCSC-FI, PHHYKY, Huld Oy, Digi Finland Oy, TAYS, Telia Finland Oy and JAMK University of Applied Sciences. The project is funded by European Regional Development Fund (ERDF), Regional Council of Central Finland, The Council of Tampere Region, and other partners mentioned before.
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Cyber security incident response processes and guidelines in healthcare environments
The project has developed cyber security incident response processes and guidelines for healthcare environments to improve and ensure the continuity of socially critical healthcare also in case of cyber attacks.
The cooperation partners were the Finnish Institute for Health and Welfare, National Emergency Supply Agency and NCSC-FI.
The project was co-funded by Business Finland.
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Scientific publications on cyber security in healthcare
Color-Optimized One-Pixel Attack Against Digital Pathology Images
This study demonstrates an advanced one-pixel attack against medical imaging. A state-of-the-art one-pixel modification is constructed with minimal effect on the pixel’s color value.
Modelling Medical Devices with Honeypots
In this paper, honeypot technology is studied for the healthcare domain. (Honeypots are traps designed to provide information on threats to an information system.)
Model for Cyber Security Information Sharing in Healthcare Sector
In this paper, the developed model with simulation results is presented, and also tested in real life scenarios within an existing project.
One-Pixel Attacks Against Medical Imaging
This paper explores the applicability of one-pixel attacks against medical imaging, in which only one pixel of an image is changed so that it fools the classifying artificial intelligence model.
Key Elements of On-Line Cyber Security Exercise and Survey of Learning During the On-Line Cyber Security Exercise
In this research, the learning experience during the state-of-the-art on-line remote cyber security exercise is studied.
One-Pixel Attack Deceives Computer-Assisted Diagnosis of Cancer
Computer vision and machine learning can be used to automate various tasks in cancer diagnostic and detection. If an attacker can manipulate the automated processing, the results can be devastating and in the worst case lead to wrong diagnosis and treatment.
