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Graphical event model learning and verification for security assessment

Dimitri Antakly 1 Benoit Delahaye 1, 2 Philippe Leray 1, 3
2 AeLoS - Architectures et Logiciels Sûrs
LS2N - Laboratoire des Sciences du Numérique de Nantes
3 DUKe - Data User Knowledge
LS2N - Laboratoire des Sciences du Numérique de Nantes
Abstract : The main objective of our work is to assess the security of a given real world system by verifying whether this system satisfies given properties and, if not, how far it is from satisfying them. We are interested in performing formal verification of this system based on event sequences collected from its execution. In this paper, we propose a preliminary model-based approach where a Graphical Event Model (GEM), learned from the event streams, is considered to be representative of the underlying system. This model is then used to check a certain security property. If the property is not verified, we also propose a search methodology to find another close model that satisfies it. Our approach is generic with respect to the verification procedure and the notion of distance between models. For the sake of completeness, we propose a distance measure between GEMs that allows to give an insight on how far our real system is from verifying the given property. The interest of this approach is illustrated with a toy example.
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Submitted on : Wednesday, April 15, 2020 - 11:25:59 AM
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Dimitri Antakly, Benoit Delahaye, Philippe Leray. Graphical event model learning and verification for security assessment. 32th International Conference on Industrial, Engineering, Other Applications of Applied Intelligent Systems (IEA/AIE 2019), 2019, Graz, Austria. pp.245-252, ⟨10.1007/978-3-030-22999-3_22⟩. ⟨hal-02129161⟩



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