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ARES: Automated Risk Estimation in Smart Sensor Environments
Industry 4.0 adoption demands integrability, interoperability, composability, and security. Currently, integrability, interoperability and composability are addressed by next-generation approaches for enterprise systems integration such as model-based standards, ontology, business process model life...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7472351/ https://www.ncbi.nlm.nih.gov/pubmed/32824471 http://dx.doi.org/10.3390/s20164617 |
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author | Dimitriadis, Athanasios Flores, Jose Luis Kulvatunyou, Boonserm Ivezic, Nenad Mavridis, Ioannis |
author_facet | Dimitriadis, Athanasios Flores, Jose Luis Kulvatunyou, Boonserm Ivezic, Nenad Mavridis, Ioannis |
author_sort | Dimitriadis, Athanasios |
collection | PubMed |
description | Industry 4.0 adoption demands integrability, interoperability, composability, and security. Currently, integrability, interoperability and composability are addressed by next-generation approaches for enterprise systems integration such as model-based standards, ontology, business process model life cycle management and the context of business processes. Security is addressed by conducting risk management as a first step. Nevertheless, security risks are very much influenced by the assets that the business processes are supported. To this end, this paper proposes an approach for automated risk estimation in smart sensor environments, called ARES, which integrates with the business process model life cycle management. To do so, ARES utilizes standards for platform, vulnerability, weakness, and attack pattern enumeration in conjunction with a well-known vulnerability scoring system. The applicability of ARES is demonstrated with an application example that concerns a typical case of a microSCADA controller and a prototype tool called Business Process Cataloging and Classification System. Moreover, a computer-aided procedure for mapping attack patterns-to-platforms is proposed, and evaluation results are discussed revealing few limitations. |
format | Online Article Text |
id | pubmed-7472351 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-74723512020-09-04 ARES: Automated Risk Estimation in Smart Sensor Environments Dimitriadis, Athanasios Flores, Jose Luis Kulvatunyou, Boonserm Ivezic, Nenad Mavridis, Ioannis Sensors (Basel) Article Industry 4.0 adoption demands integrability, interoperability, composability, and security. Currently, integrability, interoperability and composability are addressed by next-generation approaches for enterprise systems integration such as model-based standards, ontology, business process model life cycle management and the context of business processes. Security is addressed by conducting risk management as a first step. Nevertheless, security risks are very much influenced by the assets that the business processes are supported. To this end, this paper proposes an approach for automated risk estimation in smart sensor environments, called ARES, which integrates with the business process model life cycle management. To do so, ARES utilizes standards for platform, vulnerability, weakness, and attack pattern enumeration in conjunction with a well-known vulnerability scoring system. The applicability of ARES is demonstrated with an application example that concerns a typical case of a microSCADA controller and a prototype tool called Business Process Cataloging and Classification System. Moreover, a computer-aided procedure for mapping attack patterns-to-platforms is proposed, and evaluation results are discussed revealing few limitations. MDPI 2020-08-17 /pmc/articles/PMC7472351/ /pubmed/32824471 http://dx.doi.org/10.3390/s20164617 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Dimitriadis, Athanasios Flores, Jose Luis Kulvatunyou, Boonserm Ivezic, Nenad Mavridis, Ioannis ARES: Automated Risk Estimation in Smart Sensor Environments |
title | ARES: Automated Risk Estimation in Smart Sensor Environments |
title_full | ARES: Automated Risk Estimation in Smart Sensor Environments |
title_fullStr | ARES: Automated Risk Estimation in Smart Sensor Environments |
title_full_unstemmed | ARES: Automated Risk Estimation in Smart Sensor Environments |
title_short | ARES: Automated Risk Estimation in Smart Sensor Environments |
title_sort | ares: automated risk estimation in smart sensor environments |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7472351/ https://www.ncbi.nlm.nih.gov/pubmed/32824471 http://dx.doi.org/10.3390/s20164617 |
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