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A Thermodynamic Assessment of the Cyber Security Risk in Healthcare Facilities

2020-05-18Conference paper Open access
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datacite.subject.fosEngenharia e Tecnologia::Engenharia Eletrotécnica, Eletrónica e Informática
datacite.subject.fosEngenharia e Tecnologia::Outras Engenharias e Tecnologias
dc.contributor.authorFernandes, Filipe
dc.contributor.authorAlves, Victor
dc.contributor.authorMachado, Joana
dc.contributor.authorMiranda, Filipe
dc.contributor.authorVicente, Dinis
dc.contributor.authorRibeiro, Jorge
dc.contributor.authorVicente, Henrique
dc.contributor.authorNeves, José
dc.date.accessioned2025-10-17T17:43:12Z
dc.date.available2025-10-17T17:43:12Z
dc.date.issued2020-05-18
dc.descriptionConference name - 8th World Conference on Information Systems and Technologies, WorldCIST 2020; Conference date - 7 April 2020 - 10 April 2020; Conference code - 240259
dc.descriptionEISBN - 9783030456979
dc.description.abstractOver the last decades a number of guidelines have been proposed for best practices, frameworks, and cyber risk assessment in present computational environments. In order to improve cyber security vulnerability, in this work it is proposed and characterized a feasible methodology for problem solving that allows for the evaluation of cyber security in terms of an estimation of its entropic state, i.e., a predictive evaluation of its risk and vulnerabilities, or in other words, the cyber security level of such ecosystem. The analysis and development of such a model is based on a line of logical formalisms for Knowledge Representation and Reasoning, consistent with an Artificial Neural Networks approach to computing, a model that considers the cause behind the action.eng
dc.description.sponsorshipThis work has been supported by FCT – Fundação para a Ciência e Tecnologia within the R&D Units Project Scope: UIDB/00319/2020.
dc.identifier.citationFernandes, F. et al. (2020). A Thermodynamic Assessment of the Cyber Security Risk in Healthcare Facilities. In: Rocha, Á., Adeli, H., Reis, L., Costanzo, S., Orovic, I., Moreira, F. (eds) Trends and Innovations in Information Systems and Technologies. WorldCIST 2020. Advances in Intelligent Systems and Computing, vol 1161. Springer, Cham. https://doi.org/10.1007/978-3-030-45697-9_44.
dc.identifier.doi10.1007/978-3-030-45697-9_44
dc.identifier.eissn2194-5365
dc.identifier.isbn9783030456962
dc.identifier.isbn9783030456979
dc.identifier.issn2194-5357
dc.identifier.urihttp://hdl.handle.net/10400.8/14308
dc.language.isoeng
dc.peerreviewedyes
dc.publisherSpringer Nature
dc.relationALGORITMI Research Center
dc.relation.hasversionhttps://link.springer.com/chapter/10.1007/978-3-030-45697-9_44
dc.relation.ispartofAdvances in Intelligent Systems and Computing
dc.relation.ispartofTrends and Innovations in Information Systems and Technologies
dc.rights.uriN/A
dc.subjectEntropy
dc.subjectCyber security
dc.subjectLogic Programming
dc.subjectKnowledge Representation and Reasoning
dc.subjectArtificial Neural Networks
dc.titleA Thermodynamic Assessment of the Cyber Security Risk in Healthcare Facilitieseng
dc.typeconference paper
dspace.entity.typePublication
oaire.awardTitleALGORITMI Research Center
oaire.awardURIhttp://hdl.handle.net/10400.8/14247
oaire.citation.conferenceDate2020-04
oaire.citation.conferencePlaceBudva, Montenegro
oaire.citation.titleAdvances in Intelligent Systems and Computing
oaire.fundingStreamConcurso de avaliação no âmbito do Programa Plurianual de Financiamento de Unidades de I&D (2017/2018) - Financiamento Base
oaire.versionhttp://purl.org/coar/version/c_be7fb7dd8ff6fe43
person.familyNameVicente
person.givenNameDinis
person.identifier.orcid0000-0002-5856-1279
relation.isAuthorOfPublication5d972595-cb7a-47d3-a2b1-94e6c8840674
relation.isAuthorOfPublication.latestForDiscovery5d972595-cb7a-47d3-a2b1-94e6c8840674
relation.isProjectOfPublication69ebc407-cbd1-4057-9da5-59997a73bcb9
relation.isProjectOfPublication.latestForDiscovery69ebc407-cbd1-4057-9da5-59997a73bcb9

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Over the last decades a number of guidelines have been proposed for best practices, frameworks, and cyber risk assessment in present computational environments. In order to improve cyber security vulnerability, in this work it is proposed and characterized a feasible methodology for problem solving that allows for the evaluation of cyber security in terms of an estimation of its entropic state, i.e., a predictive evaluation of its risk and vulnerabilities, or in other words, the cyber security level of such ecosystem. The analysis and development of such a model is based on a line of logical formalisms for Knowledge Representation and Reasoning, consistent with an Artificial Neural Networks approach to computing, a model that considers the cause behind the action.
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