Project description
'Shielding' healthcare organisations against cyberattacks
Cybersecurity is a challenge for healthcare, an essential service that uses a vast amount of sensitive personal data. The average healthcare organisation spends more than EUR 1 million to recover from an attack. A rise in cybersecurity breaches in healthcare organisations is compromising patient privacy and posing a danger to patient safety. For instance, the use of wireless medical devices may be hacked by cybercriminals to harm a patient. The EU-funded ProTego project will develop a toolkit for healthcare organisations to better assess and reduce cybersecurity risks related to remote devices’ access to electronic health record data. It will introduce three main advances over current approaches: extensive use of machine intelligence, advanced data protection measures and innovation protocols for stakeholder education.
Objective
Health care is an essential service that uses a great deal of sensitive personal data which has a high black market value being a lucrative target for data theft and ransomware attacks.The EU NIS Directive (EU 2016/1148) and GDPR (EU 2016/679) will harmonize and improve information security in Europe. Both require relevant ICT infrastructure operators to perform risk assessments, introduce appropriate security measures to manage identified risks, and report security breaches. Unfortunately, risk-based approaches are notoriously difficult to implement in a consistent and comprehensive fashion. They depend on a high level of understanding of both cybersecurity and of the system or network to be protected, are labour intensive and costly and typically done by small teams. This is increasingly inappropriate as health care providers introduce IoT systems, cloud services and (in the near future) 5G networks to provide services in which patients are more engaged, may own some of the devices used, and want access in hospitals, on the move or at home. The ProTego project will develop a toolkit and guidelines to help health care systems users address cybersecurity risks in this new environment by introducing 3 main advances over current approaches: Extensive use of machine intelligence: a combination of machine inference exploiting a priory knowledge for security-by-design, and machine learning from data for run-time threat detection and diagnosis; Advanced data protection measures: advanced encryption techniques and hardware based full memory encryption, and multi-stakeholder IAM to control access to and by user devices, to protect data at rest and provide ultra-secure data exchange portals; Innovative protocols for stakeholder education: using security-by-design analysis to target training and support stakeholders to contribute to networok overall security.The toolkit will be integrated and validated in IoT and BYOD-based case studies at two hospitals.
Fields of science
- engineering and technologyelectrical engineering, electronic engineering, information engineeringinformation engineeringtelecommunicationstelecommunications networksmobile network5G
- natural sciencescomputer and information sciencesinternetinternet of things
- natural sciencescomputer and information sciencescomputer securitydata protection
- natural sciencescomputer and information sciencesartificial intelligencemachine learningdeep learning
- natural sciencescomputer and information sciencesdata sciencedata exchange
Programme(s)
Funding Scheme
RIA - Research and Innovation actionCoordinator
28050 Madrid
Spain