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Consortium

UPRC

UPRC will serve as the project coordinator and leader of Work Package 1. It will focus on bridging the gap between academic research and market-driven innovation. UPRC will lead the development of a medical IoT malware classifier and organize an adversarial AI hackathon. It has extensive experience organizing Capture The Flag challenges and will play a key role in dissemination efforts.

PDM

PDM, an SME, has strong expertise in cybersecurity threats and vulnerabilities, particularly in Intrusion Detection Systems (IDS). It will lead research activities in Work Package 2 and contribute to the development of an AI-powered IDS for medical networks. Additionally, PDM will oversee system integration and design the evaluation framework for the project.

IURC

IURC will manage the technical aspects of the project and lead Work Package 5. It will contribute to the development of robust AI models for malware classification and intrusion detection. IURC will also facilitate interdisciplinary work between medical analysis, cybersecurity, and AI, while organizing a summer school in Corfu as a major dissemination event.

BioIRC

BioIRC specializes in biomechanics, bioinformatics, and medical image processing. It will develop federated learning techniques to improve diagnostic accuracy while ensuring patient data privacy. BioIRC will also lead the demonstration of Use Case 1, focusing on AI attacks and defenses for Covid-19 diagnosis.

CNIT

CNIT, a major research institution in Italy, is highly skilled in offensive cybersecurity. It will lead Work Package 4 and contribute to the development of adversarial samples for antivirus evasion and IDS bypass. CNIT will also design the AI threat model and help develop robust detection models for medical IoT malware.

Ulusófona

Ulusófona, the largest private university in Portugal, will design the federated learning scheme for medical data analysis and contribute to IDS development. It will also play a role in ensuring data privacy in the project’s efforts to develop secure and privacy-preserving systems for medical IoT networks.

UNIKG

UNIKG, with expertise in bioengineering, will lead the design of robust AI models for medical image analysis. It will also collaborate with Ulusófona to develop a federated learning framework for distributed medical data analysis. UNIKG will contribute to adversarial AI work in the project.

INFOLYSIS

INFOLYSIS, an SME specializing in AI and deep learning, will contribute to the design and implementation of AI models for federated diagnosis, malware detection, and intrusion detection. INFOLYSIS will also lead the dissemination activities, ensuring that the project’s results are widely shared and communicated.

NUSTPB

NUSTPB, one of Romania's largest technical universities, will collaborate on the development of AI models for medical IoT antivirus and IDS systems. It will lead Work Package 3 and contribute to the design of the AI threat model. NUSTPB will also provide cloud infrastructure for AI model development.