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Creating MAL Instances Using ArchiMate on the Example of Attacks on Power Plants and Power Grids
KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Network and Systems Engineering.ORCID iD: 0000-0003-0478-9347
Universität Duisburg-Essen, Duisburg, Germany.
KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Network and Systems Engineering.ORCID iD: 0000-0001-8287-3160
Institute for High Voltage Technology, RWTH Aachen University, Aachen, Germany.
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2019 (English)In: Proceeding of the 2019 IEEE 23rd International Enterprise Distributed Object Computing Conference (EDOC), IEEE, 2019Conference paper, Published paper (Refereed)
Abstract [en]

Cyber-attacks on power assets can have disastrous consequences for individuals, regions, and whole nations. In order to respond to these threats, the assessment of power grids' and plants' cyber security can foster a higher degree of safety for the whole infrastructure dependent on power. Hitherto, we propose the use of attack simulations based on system architecture models. To reduce the effort of creating new attack graphs for each system of a given type, domain-specific attack languages may be employed. They codify common attack logics of the considered domain. Previously, MAL (the Meta Attack Language) was proposed, which serves as a framework to develop domain specific attack languages. We extend the tool set of MAL by developing an approach to model security domains in ArchiMate notation. Next, those models are used to create a MAL instance, which reflects the concepts modeled in ArchiMate. These instances serve as input to simulate attacks on certain systems. To show the applicability of our approach, we conduct two case studies in the power domain. On the one hand, we model a thermal power plant and possible attacks on it. On the other hand, we use the attack on the Ukrainian power grid for our case study.

Place, publisher, year, edition, pages
IEEE, 2019.
Series
Proceedings IEEE International Enterprise Distributed Object Computing Conference, ISSN 2325-6354, E-ISSN 2325-6362
National Category
Computer and Information Sciences
Identifiers
URN: urn:nbn:se:kth:diva-266444DOI: 10.1109/EDOC.2019.00020Scopus ID: 2-s2.0-85078238227ISBN: 978-1-7281-2702-6 (electronic)ISBN: 978-1-7281-2703-3 (print)OAI: oai:DiVA.org:kth-266444DiVA, id: diva2:1384533
Conference
2019 IEEE 23rd International Enterprise Distributed Object Computing Conference (EDOC), 28-31 Oct. 2019
Note

QC 20200120

Available from: 2020-01-10 Created: 2020-01-10 Last updated: 2020-03-09Bibliographically approved

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Hacks, SimonKatsikeas, Sotirios

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