Please use this identifier to cite or link to this item: https://ruomo.lib.uom.gr/handle/7000/1049
Title: ARES: Automated Risk Estimation in Smart Sensor Environments
Authors: Dimitriadis, Athanasios
Flores, Jose Luis
Kulvatunyou, Boonserm
Ivezic, Nenad
Mavridis, Ioannis
Type: Article
Subjects: FRASCATI::Engineering and technology::Electrical engineering, Electronic engineering, Information engineering
FRASCATI::Engineering and technology::Electrical engineering, Electronic engineering, Information engineering
Keywords: Common Security Standards
business process context
information system risk assessment
smart sensor environments
Issue Date: 17-Aug-2020
Publisher: MDPI
Source: Sensors (Basel, Switzerland)
Volume: 20
Issue: 16
First Page: 4617
Abstract: 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.
URI: https://doi.org/10.3390/s20164617
https://ruomo.lib.uom.gr/handle/7000/1049
ISSN: 1424-8220
Electronic ISSN: 1424-8220
Other Identifiers: 10.3390/s20164617
Appears in Collections:Department of Applied Informatics

Files in This Item:
File Description SizeFormat 
sensors-20-04617.pdf3,78 MBAdobe PDFView/Open


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.