A Self-Organized Swarm Intelligence Solution for Healthcare ICT Security

The healthcare sector has undergone a transformative evolution through the integration of advanced technologies such as IoT, Cloud Computing, and Big Data. This progression, starting with electronic health records, now includes a diverse array of digital tools, from medical apps to wearables. These innovations have significantly improved patient experiences and outcomes, forming extensive Health Care InformationInfrastructures (HCIIs). Consequently, the security of interdependent HCIIs and Health Care Supply Chain (HCSCS) is intrinsically linked to the security of each individual HCIIs that constitute the collective network. Currently, HCIIs face vulnerabilities due to reliance on isolated cybersecurity products, necessitating a unified security strategy. Recognizing the criticality of assets, prioritizing emerging solutions becomes crucial to mitigating complexity. The evolving landscape of cyber threats in healthcare demands collaboration among European health and cybersecurity experts to establish policies and standards, elevating security maturity across the EU. The proposed solution in this study represents a cutting-edge approach to healthcare cybersecurity. It enhances threat detection, analysis, and privacy awareness in the digital healthcare ecosystem through a Dynamic Situational Awareness Framework. This empowers stakeholders to recognize and respond to cyber risks effectively, including advanced persistent threats and daily incidents. The solution facilitates secure incident-related information exchange, strengthening the security and resilience of modern digital healthcare systems and supply chain services. The innovative approach draws inspiration from biological swarm formations, integrating security engineering, privacy engineering,
and artificial intelligence. By creating a highly interconnected intelligence system, it enables local interactions and management in healthcare environments. Employing bio-inspired techniques and large-group decision-making models enhances communication and coordination in complex, distributed networks. The framework prioritizes scalability and fault-tolerance, streamlining investigation activities and fostering dynamic intelligence and collective decision-making within healthcare ecosystems.