The healthcare ecosystem is complex by its inherent nature, which consists of a heterogeneous set of actors, entities, and sub-systems to deliver multidisciplinary and collaborative health services. The in- creased use of connected medical devices makes such an ecosystem more vulnerable and increases the cyber-attack surface. Traditional security methods are insufficient to deal with such a high degree of interconnected medical and IoT devices. There is a need for security approaches based on concepts of collaboration, cooperation, autonomy and dynamism to en- sure timely security of the whole healthcare ecosystem. This work adopts swarm-based principles with multi-agent systems to meet collaboration, distribution and robustness requirements, thus improving the healthcare ecosystem's security. The paper presents a swarm-based agent-to-agent communication model founded on the collaboration among primary and supervisor agents to acquire new knowledge related to the healthcare ecosystem. The proposed model is based on the direct collaboration be- tween primary agents that provides supervisor agents with local security- related information and the indirect collaboration between supervisor agents that exchange stigmergic information through the environment to make a collectively informed decision. The communication model is implemented using the BDI (Belief-Desire-Intention) approach. The pre- liminary results show the communication model's robustness, scalability and responsiveness for securing the healthcare ecosystem.

Swarm Intelligence based multi-agent communication model for securing healthcare ecosystem

Patrizia Ribino;Mario Ciampi;
2022

Abstract

The healthcare ecosystem is complex by its inherent nature, which consists of a heterogeneous set of actors, entities, and sub-systems to deliver multidisciplinary and collaborative health services. The in- creased use of connected medical devices makes such an ecosystem more vulnerable and increases the cyber-attack surface. Traditional security methods are insufficient to deal with such a high degree of interconnected medical and IoT devices. There is a need for security approaches based on concepts of collaboration, cooperation, autonomy and dynamism to en- sure timely security of the whole healthcare ecosystem. This work adopts swarm-based principles with multi-agent systems to meet collaboration, distribution and robustness requirements, thus improving the healthcare ecosystem's security. The paper presents a swarm-based agent-to-agent communication model founded on the collaboration among primary and supervisor agents to acquire new knowledge related to the healthcare ecosystem. The proposed model is based on the direct collaboration be- tween primary agents that provides supervisor agents with local security- related information and the indirect collaboration between supervisor agents that exchange stigmergic information through the environment to make a collectively informed decision. The communication model is implemented using the BDI (Belief-Desire-Intention) approach. The pre- liminary results show the communication model's robustness, scalability and responsiveness for securing the healthcare ecosystem.
2022
Istituto di Calcolo e Reti ad Alte Prestazioni - ICAR
Inglese
Proceedings of the International Conference on Ubiquitous Computing & Ambient Intelligence (UCAmI 2022)
14th International Conference on Ubiquitous Computing and Ambient Intelligence (UCAmI 2022)
50
61
12
Sì, ma tipo non specificato
30/11/2022 - 02/12/2022
Córdoba (Spain)
Cyber-Security · Healthcare Information Infrastructure · Multi-Agent System · Communication Model · Swarm Intelligence
4
restricted
Ribino, Patrizia; Islam, Shareeful; Ciampi, Mario; Papastergiou, Spyros
273
info:eu-repo/semantics/conferenceObject
04 Contributo in convegno::04.01 Contributo in Atti di convegno
   A Dynamic and Self-Organized Artificial Swarm Intelligence Solution for Security and Privacy Threats in Healthcare ICT Infrastructures
   AI4HEALTHSEC
   H2020
   883273
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.14243/420206
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