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https://doi.org/10.54941/ahfe1...
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A Self-Organized Swarm Intelligence Solution for Healthcare ICT Security

Authors: Kitty Kioskli; Spyridon Papastergiou; Theo Fotis; Stefano Silvestri; Haralambos Mouratidis;

A Self-Organized Swarm Intelligence Solution for Healthcare ICT Security

Abstract

The healthcare sector has undergone significant transformation in recent years, driven by the adoption of advanced medical technologies like IoT, Cloud Computing, and Big Data. This evolution began with the integration of electronic health records and has expanded to encompass a wide range of digital tools, from medical apps to wearables. These technological advancements have played a crucial role in enhancing patient experiences and outcomes. As healthcare technology has become increasingly interconnected, both physically and in the cyber realm, it has evolved into vast Health Care Information Infrastructures (HCIIs). These HCIIs are of paramount importance due to their critical role in people's well-being and safety. Any disruption, whether through direct actions like medical errors, or indirect actions such as altering patient records can have severe consequences for patient health. Currently, HCIIs are vulnerable because they often rely on isolated cybersecurity products. There is a pressing need to establish a comprehensive security strategy that can coordinate various security components to detect system vulnerabilities and sophisticated attacks. To address this complex challenge, it is essential to break down cybersecurity concerns in the healthcare sector based on the criticality of their assets. Prioritizing emerging solutions in this manner will help mitigate the complexity of the problem. Cyberattacks on the healthcare sector have become increasingly sophisticated and involve not only technical vulnerabilities but also social engineering tactics that exploit individuals with limited technical knowledge. European health and cybersecurity experts must collaborate to develop policies and standards that elevate security maturity throughout the EU. Ultimately, cybersecurity solutions in healthcare should not only enhance security but also have a positive business impact, enabling new services, collaborations, and market opportunities. The proposed solution in this study, represents a state-of-the-art approach to enhancing cybersecurity within HCIIs. It improves the detection and analysis of cyber threats and increases awareness of privacy and security risks in the digital healthcare ecosystem. By providing a Dynamic Situational Awareness Framework, the solution empowers stakeholders in the healthcare sector to recognize, model, and respond to cyber risks, including advanced persistent threats and daily cybersecurity incidents. Additionally, it facilitates the secure exchange of incident-related information aiming to strengthen the security and resilience of modern digital healthcare systems and the associated medical supply chain services. The proposed solution extends the frontiers of various research fields, including security engineering, privacy engineering, and artificial intelligence. Drawing inspiration from biological swarm formations, it brings together these disciplines to empower stakeholders in digital healthcare ecosystems. This leads to the creation of a highly interconnected and advanced intelligence system, comprised of simple nodes or groups of nodes, enabling local interactions and management of healthcare environments. By employing bio-inspired techniques and large-group decision-making models, the framework enhances communication and coordination in complex, distributed networks typical of interconnected healthcare infrastructures. It prioritizes scalability and fault-tolerance, allowing coordinated actions without a central coordinator. This approach streamlines investigation activities within healthcare ecosystems, fostering dynamic intelligence and collective decision-making, even when individual nodes lack a complete view of the situation.

Country
Italy
Keywords

Cybersecurity, Swarm Intelligence, Human Factors, Healthcare

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
1
Average
Average
Average
Green
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