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https://ruomo.lib.uom.gr/handle/7000/1049
Τίτλος: | ARES: Automated Risk Estimation in Smart Sensor Environments |
Συγγραφείς: | Dimitriadis, Athanasios Flores, Jose Luis Kulvatunyou, Boonserm Ivezic, Nenad Mavridis, Ioannis |
Τύπος: | Article |
Θέματα: | FRASCATI::Engineering and technology::Electrical engineering, Electronic engineering, Information engineering FRASCATI::Engineering and technology::Electrical engineering, Electronic engineering, Information engineering |
Λέξεις-Κλειδιά: | Common Security Standards business process context information system risk assessment smart sensor environments |
Ημερομηνία Έκδοσης: | 17-Αυγ-2020 |
Εκδότης: | MDPI |
Πηγή: | Sensors (Basel, Switzerland) |
Τόμος: | 20 |
Τεύχος: | 16 |
Πρώτη Σελίδα: | 4617 |
Επιτομή: | 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 |
Ηλεκτρονικό ISSN: | 1424-8220 |
Αλλοι Προσδιοριστές: | 10.3390/s20164617 |
Εμφανίζεται στις Συλλογές: | Τμήμα Εφαρμοσμένης Πληροφορικής |
Αρχεία σε αυτό το Τεκμήριο:
Αρχείο | Περιγραφή | Μέγεθος | Μορφότυπος | |
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sensors-20-04617.pdf | 3,78 MB | Adobe PDF | Προβολή/Ανοιγμα |
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