@inproceedings{10.1007/978-3-031-95497-9_7,
author = {Ghosh, Bineet and Andr\'{e}, \'{E}tienne},
title = {Probabilistic Safety Verification of Distributed Systems: A Statistical Approach for Monitoring},
year = {2025},
isbn = {978-3-031-95496-2},
publisher = {Springer-Verlag},
address = {Berlin, Heidelberg},
url = {https://doi.org/10.1007/978-3-031-95497-9_7},
doi = {10.1007/978-3-031-95497-9_7},
abstract = {With the increasing autonomous capabilities of distributed cyber-physical systems, the complexity of their models also increases significantly, thus continually posing challenges to existing formal methods for safety verification. In contrast to model checking, monitoring emerges as an effective lightweight, yet practical verification technique capable of delivering results of practical importance with better scalability. Monitoring involves analyzing logs from an actual system to determine whether a specification (such as a safety property) is violated. Monitoring techniques, such as those using reachability methods, may fail to produce results when dealing with complex models like Deep Neural Networks (DNNs). We propose here a novel statistical approach for monitoring that is able to generate results with high probabilistic guarantees. We evaluate our monitoring technique on three case studies.},
booktitle = {Formal Techniques for Distributed Objects, Components, and Systems: 45th IFIP WG 6.1 International Conference, FORTE 2025, Held as Part of the 20th International Federated Conference on Distributed Computing Techniques, DisCoTec 2025, Lille, France, June 16–20, 2025, Proceedings},
pages = {114–133},
numpages = {20},
keywords = {Autonomous Systems, Monitoring, Statistical Verification},
location = {Lille, France}
}