38 Part I: Data-driven Inference of Fault Tree models BEs in FD identical values to bk in the dataset Dresults in the same overall system status, fFD(bk)=f D(bk). Problem statement. Given a failure dataset D='bk f(bk)(, create an FTFD that is both 1. small, i.e., the number of nodes ωs is minimal, and 2. accurate, i.e., the structure function fFD of the FT coincides with the given failure dataset fD(bk). I.5 References Carpignano, A. and A. Poucet (1994). “Computer assisted fault tree construction: a review of methods and concerns”. In: Reliability Engineering & System Safety 44.3, pp. 265–278. doi: 10.1016/0951-8320(94)90018-3. Dickerson, C. E., R. Roslan, and S. Ji (2018). “A formal transformation method for automated fault tree generation from a UML activity model”. In: IEEE Transactions on Reliability 67 (3), pp. 1219–1236. doi: 10.1109/TR.2018.2849013. Dorfhuber, F., J. Eisentraut, and J. K%etínsk& (2023). “Learning Attack Trees by Genetic Algorithms”. In: Theoretical Aspects of Computing – ICTAC 2023. Ed. by E. Ábrahám, C. Dubsla!, and S. L. T. Tarifa. Cham: Springer Nature Switzerland, pp. 55–73. isbn: 978-3-031-47963-2. doi: 10.1007/978-3-031-47963-2_5. Jimenez-Roa, L. A., N. Rusnac, M. Volk, and M. Stoelinga (2024). “Fault Tree Inference Using Multi-objective Evolutionary Algorithms and Confusion Matrix-Based Metrics”. In: Formal Methods for Industrial Critical Systems. Ed. by A. E. Haxthausen and W. Serwe. Cham: Springer Nature Switzerland, pp. 80–96. isbn: 978-3-031-68150-9. doi: 10.1007/978-3-031-68150-9_5. Jimenez-Roa, L. A., T. Heskes, T. Tinga, and M. Stoelinga (2023). “Automatic Inference of Fault Tree Models Via Multi-Objective Evolutionary Algorithms”. In: IEEE Transactions on Dependable and Secure Computing 20.4, pp. 3317–3327. doi: 10.1109/TDSC. 2022.3203805. Jimenez-Roa, L. A., M. Volk, and M. Stoelinga (2022). “Data-Driven Inference of Fault Tree Models Exploiting Symmetry and Modularization”. In: Computer Safety, Reliability, and Security. Ed. by M. Trapp, F. Saglietti, M. Spisländer, and F. Bitsch. Cham: Springer International Publishing, pp. 46–61. doi: 10.1007/978-3-031-14835-4_4. Latif-Shabgahi, G. (2002). “Comparing selected knowledge-based fault tree construction tools”. In: Proceedings of the IASTED International Conference on Intelligent Systems and Control. Lazarova-Molnar, S., P. Niloofar, and G. K. Barta (2020). “Data-Driven Fault Tree Modeling for Reliability Assessment of Cyber-Physical Systems”. In: 2020 Winter Simulation Conference (WSC), pp. 2719–2730. doi: 10.1109/WSC48552.2020.9383882. Linard, A., M. Bueno, D. Bucur, and M. Stoelinga (2020). “Induction of fault trees through Bayesian networks”. In: Proceedings of the 29th European Safety and Reliability Conference, ESREL 2019, pp. 910–917. doi: 10.3850/978-981-11-2724-3_0596-cd. Linard, A., D. Bucur, and M. Stoelinga (2019). “Fault Trees from Data: E’cient Learning with an Evolutionary Algorithm”. In: International Symposium on Dependable Software Engineering: Theories, Tools, and Applications. Vol. 11951 LNCS, pp. 19–37. doi: 10.1007/978-3-030-35540-1_2.
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