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CC BY-NC-ND 4.0 · Methods Inf Med 2021; 60(S 02): e89-e102
DOI: 10.1055/s-0041-1735621
Original Article for a Focus Theme

Towards the Representation of Network Assets in Health Care Environments Using Ontologies

Lucía Prieto Santamaría
1   ETS Ingenieros Informáticos, Universidad Politécnica de Madrid, Madrid, Spain
2   Centro de Tecnología Biomédica, Universidad Politécnica de Madrid, Madrid, Spain
,
David Fernández Lobón
2   Centro de Tecnología Biomédica, Universidad Politécnica de Madrid, Madrid, Spain
,
Antonio Jesús Díaz-Honrubia
1   ETS Ingenieros Informáticos, Universidad Politécnica de Madrid, Madrid, Spain
2   Centro de Tecnología Biomédica, Universidad Politécnica de Madrid, Madrid, Spain
,
Ernestina Menasalvas Ruiz
1   ETS Ingenieros Informáticos, Universidad Politécnica de Madrid, Madrid, Spain
2   Centro de Tecnología Biomédica, Universidad Politécnica de Madrid, Madrid, Spain
,
Sokratis Nifakos
3   Department of Learning, Informatics, Management and Ethics, Karolinska Institute, Stockholm, Sweden
,
Alejandro Rodríguez-González
1   ETS Ingenieros Informáticos, Universidad Politécnica de Madrid, Madrid, Spain
2   Centro de Tecnología Biomédica, Universidad Politécnica de Madrid, Madrid, Spain
› Institutsangaben Funding The research work presented in this article has been supported by the European Commission under the Horizon 2020 Programme, through funding of the CUREX project (G.A. n 826404).
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Abstract

Objectives The aim of the study is to design an ontology model for the representation of assets and its features in distributed health care environments. Allow the interchange of information about these assets through the use of specific vocabularies based on the use of ontologies.

Methods Ontologies are a formal way to represent knowledge by means of triples composed of a subject, a predicate, and an object. Given the sensitivity of network assets in health care institutions, this work by using an ontology-based representation of information complies with the FAIR principles. Federated queries to the ontology systems, allow users to obtain data from multiple sources (i.e., several hospitals belonging to the same public body). Therefore, this representation makes it possible for network administrators in health care institutions to have a clear understanding of possible threats that may emerge in the network.

Results As a result of this work, the “Software Defined Networking Description Language—CUREX Asset Discovery Tool Ontology” (SDNDL-CAO) has been developed. This ontology uses the main concepts in network assets to represent the knowledge extracted from the distributed health care environments: interface, device, port, service, etc.

Conclusion The developed SDNDL-CAO ontology allows to represent the aforementioned knowledge about the distributed health care environments. Network administrators of these institutions will benefit as they will be able to monitor emerging threats in real-time, something critical when managing personal medical information.

Keywords

network assets - ontologies - health care cybersecurity - knowledge representation


Publikationsverlauf

Eingereicht: 14. April 2021

Angenommen: 20. Juli 2021

Artikel online veröffentlicht:
05. Oktober 2021

© 2021. The Author(s). This is an open access article published by Thieme under the terms of the Creative Commons Attribution-NonDerivative-NonCommercial License, permitting copying and reproduction so long as the original work is given appropriate credit. Contents may not be used for commercial purposes, or adapted, remixed, transformed or built upon. (https://creativecommons.org/licenses/by-nc-nd/4.0/)

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  • References

  • 1 Sørensen C, de Reuver M, Basole RC. Mobile Platforms and Ecosystems. J Inf Technol 2015; 30 (03) 195-197
    CrossrefPubMedGoogle Scholar
  • 2 Tully J, Selzer J, Phillips JP, O'Connor P, Dameff C. Healthcare challenges in the era of cybersecurity. Health Secur 2020; 18 (03) 228-231
    CrossrefPubMedGoogle Scholar
  • 3 Critical Infrastructures and Services. Accessed April 13, 2021 at: https://www.enisa.europa.eu/topics/critical-information-infrastructures-and-services
    PubMedGoogle Scholar
  • 4 Council Directive 2008/114/EC of 8 December 2008 on the Identification and Designation of European Critical Infrastructures and the Assessment of the Need to Improve Their Protection (Text with EEA Relevance). Vol OJ L.; 2008. Accessed April 13, 2021 at: http://data.europa.eu/eli/dir/2008/114/oj/eng
    PubMedGoogle Scholar
  • 5 Shi F, Li Q, Zhu T, Ning H. A survey of data semantization in internet of things. Sensors (Basel) 2018; 18 (01) E313
    CrossrefPubMedGoogle Scholar
  • 6 Kaur K, Rani R. Managing data in healthcare information systems: many models, one solution. Computer 2015; 48 (03) 52-59
    CrossrefPubMedGoogle Scholar
  • 7 Chen P-T, Lin C-L, Wu W-N. Big data management in healthcare: adoption challenges and implications. Int J Inf Manage 2020; 53: 102078
    CrossrefPubMedGoogle Scholar
  • 8 Kolias VD, Stoitsis J, Golemati S, Nikita KS. Utilizing semantic web technologies in healthcare. In: Koutsouris D-D, Lazakidou AA. eds. Concepts and Trends in Healthcare Information Systems. Annals of Information Systems. Springer International Publishing; 2014: 9-19
    Google Scholar
  • 9 Hammad R, Barhoush M, Abed-Alguni BH. A semantic-based approach for managing healthcare big data: a survey. J Healthc Eng 2020; 2020: 8865808
    CrossrefPubMedGoogle Scholar
  • 10 Kim DJ, Hebeler J, Yoon V, Davis F. Exploring determinants of semantic web technology adoption from IT professionals' perspective: industry competition, organization innovativeness, and data management capability. Comput Human Behav 2018; 86: 18-33
    CrossrefPubMedGoogle Scholar
  • 11 Guarino N. Formal Ontologies and Information Systems. IOS Press; 1998
    Google Scholar
  • 12 Guarino N, Oberle D, Staab S. What is an ontology?. In: Staab S, Studer R. eds. Handbook on Ontologies. International Handbooks on Information Systems. Berlin, Heidelberg: Springer; 2009: 1-17
    CrossrefGoogle Scholar
  • 13 RDF - Semantic Web Standards. Published November 24, 2019. Accessed November 24, 2019 at: https://www.w3.org/RDF/
    PubMed
  • 14 SPARQL Query Language for RDF. Published November 24; 2019. Accessed November 24, 2019 at: https://www.w3.org/TR/rdf-sparql-query/
    PubMed
  • 15 Wilkinson MD, Dumontier M, Aalbersberg IJ. et al. The FAIR Guiding Principles for scientific data management and stewardship. Sci Data 2016; 3: 160018
    CrossrefPubMedGoogle Scholar
  • 16 OpenLink Virtuoso Universal Server Documentation. Published November 25, 2019. Accessed November 25, 2019 at: http://docs.openlinksw.com/virtuoso/
    PubMed
  • 17 Antoniou G, van Harmelen F. Web Ontology Language: OWL. In: Staab S, Studer R. eds. Handbook on Ontologies. International Handbooks on Information Systems. Berlin, Heidelberg: Springer; 2004: 67-92
    CrossrefGoogle Scholar
  • 18 Zhou Q, Gray AJG, McLaughlin S. ToCo: An ontology for representing hybrid telecommunication networks. In: Hitzler P, Fernández M, Janowicz K. et al, eds. The Semantic Web: 16th International Conference, ESWC 2019, Portorož, Slovenia, June 2–6, 2019. Series: Lecture Notes in Computer Science (11503). Springer; 507-522
    Google Scholar
  • 19 Sikos LF, Stumptner M, Mayer W, Howard C, Voigt S, Philp D. Representing network knowledge using provenance-aware formalisms for cyber-situational awareness. Procedia Comput Sci 2018; 126: 29-38
    CrossrefPubMedGoogle Scholar
  • 20 Mozzaquatro BA, Agostinho C, Goncalves D, Martins J, Jardim-Goncalves R. an ontology-based cybersecurity framework for the internet of things. Sensors (Basel) 2018; 18 (09) E3053
    CrossrefPubMedGoogle Scholar
  • 21 Herzog A, Shahmehri N, Duma C. An ontology of information security. IJISP 2007; 1: 1-23
    PubMedGoogle Scholar
  • 22 Voigt S, Howard C, Philp D, Penny C. Representing and reasoning about logical network topologies. In: Croitoru M, Marquis P, Rudolph S, Stapleton G. eds. Graph Structures for Knowledge Representation and Reasoning. Lecture Notes in Computer Science.. Springer International Publishing; 2018: 73-83
    Google Scholar
  • 23 Intelligent Systems Group. Software defined networking description language. Accessed March 5, 2021 at: http://www.gsi.upm.es/ontologies/sdndl/
    PubMedGoogle Scholar
  • 24 Grosso P, Dijkstra F, Van der Ham J, Laat C. Network description language–semantic web for hybrid networks. Proceedings of TERENA Networking Conference 2007. Kongens Lyngby, Copenhagen, Denmark
    PubMedGoogle Scholar
  • 25 Ghijsen M, van der Ham J, Grosso P, de Laat C. Towards an infrastructure description language for modeling computing infrastructures. Paper presented at: 2012 IEEE 10th International Symposium on Parallel and Distributed Processing with Applications; 2012: 207-214
    PubMedGoogle Scholar
  • 26 van der Ham J, Dijkstra F, Lapacz R, Brown A. The network markup language (nml) a standardized network topology abstraction for inter-domain and cross-layer network applications. Paper presented at: Proceedings of the 13th TERENA Networking Conference, Maastricht, Netherlands; 2013
    PubMedGoogle Scholar
  • 27 Batsakis S, Petrakis EGM, Tachmazidis I, Antoniou G. Temporal representation and reasoning in OWL 2. Semant Web 2017; 8 (06) 981-1000
    CrossrefPubMedGoogle Scholar