On synergies of cyber and physical security modelling in vulnerability assessment of railway systemsShow others and affiliations
2015 (English)In: Computers & electrical engineering, ISSN 0045-7906, E-ISSN 1879-0755, Vol. 47, p. 275-285Article in journal (Refereed) Published
Abstract [en]
The multifaceted nature of cyber-physical systems needs holistic study methods to detect essential aspects and interrelations among physical and cyber components. Like the systems themselves, security threats feature both cyber and physical elements. Although to apply divide et impera approaches helps handling system complexity, to consider just one aspect at a time does not provide adequate risk awareness and hence does not allow to design the most appropriate countermeasures. To support this claim, in this paper we provide a joint application of two model-driven techniques for physical and cyber-security evaluation. We apply two UML profiles, namely SecAM (for cyber-security) and CIP-VAM (for physical security), in combination. In such a way, we demonstrate the synergy between both profiles and the need for their tighter integration in the context of a reference case study from the railway domain. Graphical abstract © 2015 Elsevier Ltd.
Place, publisher, year, edition, pages
Elsevier , 2015. Vol. 47, p. 275-285
Keywords [en]
Bayesian networks, Cyber-physical systems, Generalized stochastic Petri nets, UML profile, Vulnerability assessment, Complex networks, Embedded systems, Markup languages, Mergers and acquisitions, Petri nets, Railroads, Risk perception, Stochastic systems, Unified Modeling Language, Cyber physical systems (CPSs), Model-driven techniques, Physical and cyber security, Physical elements, Physical security, Uml profiles, Vulnerability assessments, Railroad transportation
National Category
Embedded Systems
Research subject
Computer Science, Software Technology
Identifiers
URN: urn:nbn:se:mdh:diva-47833DOI: 10.1016/j.compeleceng.2015.07.011ISI: 000367637200020Scopus ID: 2-s2.0-84939487175OAI: oai:DiVA.org:mdh-47833DiVA, id: diva2:1427316
2018-06-042020-04-292020-10-14Bibliographically approved