European project RESPECT

Program: H2020-MSCA-RISE-2020

Title: RESPECT - Threat models, system resilience assessment and defensive strategy requirements and specifications

Project duration: 36 months

RESPECT's website : https://www.project-respect.eu/

Project Summary: Advances in artificial intelligence and robotics hold a promise for the radical transformation of healthcare services. Yet, the adoption of these technologies is impeded by concerns related to cybersecurity issues. The rise of security incidents is a timely reminder that not only the volume of attacks is increasing but their diversity is also expanding, posing a significant threat towards disrupting clinical care delivery. Ongoing research focuses on the use of robotics operating in healthcare spaces (surgical, service, logistics), while security aspects are not well covered in the research community.

RESPECT project aims to design and develop concrete defence strategies to ensure secure, safe, and privacy-preserving operation of indoor mobile robotics solutions for logistic applications in healthcare environments. Specifically the main research objectives of the project are:

• Explore and identify system specific cyber-physical weaknesses posing security, privacy, and safety threats, in autonomous mobile robots operating in a healthcare environment.
• Analyse surfaced vulnerability issues in conjunction with projected threats and propose defence measures and mitigation strategies towards safeguarding mobile robots operation.
• Define and standardize a set of vulnerability testing procedures and guidelines leveraging and extending the Robot Vulnerability Scoring System for safe and autonomous robotic fleet management in a “safety-critical setting”.

The project will be implemented through staff exchanges among the 11 organizations with complementary expertise in cybersecurity, healthcare, cloud computing and robotics from 7 countries across EU promoting transfer of knowledge between industry and academia.

Partners:

This project has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No 101007673