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 | Size | Format | |
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sensors-20-04617.pdf | 3,78 MB | Adobe PDF | View/Open |
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