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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References
Baier, C., Katoen, J.P.: Principles of Model Checking. MIT Press, Cambridge (2008)
Gunawardana, A., Meek, C.: Universal models of multivariate temporal point processes. In: Proceedings of the 19th International Conference on Artificial Intelligence and Statistics, pp. 556–563 (2016)
Langmead, C.J.: Generalized queries and Bayesian statistical model checking in dynamic Bayesian networks: application to personalized medicine. In: Proceedings of the 8th Annual International Conference on Computational Systems Bioinformatics, pp. 201–212. Life Sciences Society (2009)
Legay, A., Delahaye, B., Bensalem, S.: Statistical model checking: an overview. In: Barringer, H., et al. (eds.) RV 2010. LNCS, vol. 6418, pp. 122–135. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-16612-9_11
Mao, H., Chen, Y., Jaeger, M., Nielsen, T.D., Larsen, K.G., Nielsen, B.: Learning probabilistic automata for model checking. In: 2011 Eighth International Conference on Quantitative Evaluation of Systems (QEST), pp. 111–120. IEEE (2011)
Rao, V., Teh, Y.W.: Fast MCMC sampling for Markov jump processes and extensions. J. Mach. Learn. Res. 14(1), 3295–3320 (2013)
Tsamardinos, I., Brown, L.E., Aliferis, C.F.: The max-min hill-climbing Bayesian network structure learning algorithm. Mach. Learn. 65(1), 31–78 (2006)
Van Der Aalst, W.: Process Mining: Discovery, Conformance and Enhancement of Business Processes. Springer, Heidelberg (2014). https://doi.org/10.1007/978-3-642-19345-3
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Antakly, D., Delahaye, B., Leray, P. (2019). Graphical Event Model Learning and Verification for Security Assessment. In: Wotawa, F., Friedrich, G., Pill, I., Koitz-Hristov, R., Ali, M. (eds) Advances and Trends in Artificial Intelligence. From Theory to Practice. IEA/AIE 2019. Lecture Notes in Computer Science(), vol 11606. Springer, Cham. https://doi.org/10.1007/978-3-030-22999-3_22
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DOI: https://doi.org/10.1007/978-3-030-22999-3_22
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