Kropatschek, S. J., Kurniawan, K., Bhosale, P. R., Hollerer, S., Kiesling, E., & Winkler, D. (2023). Towards A Knowledge Graph-based Framework for Integrated Security and Safety Analysis in Digital Production Systems. In I. Fundulaki, K. Kouji, D. Garijo, & J. M. Gomez-Perez (Eds.), Proceedings of the ISWC 2023 Posters, Demos and Industry Tracks: From Novel Ideas to Industrial Practice co-located with 22nd International Semantic Web Conference (ISWC 2023).
The increasing interconnection of Information Technology and Operational Technology in Industry 4.0 creates new challenges and requires new approaches to ensure that production processes are executed safely and securely. Production system safety and security have therefore become critical aspects as security incidents can lead to serious problems such as production failure, equipment damage, or human injury. This paper introduces a knowledge-graph-based framework for safety and security analysis that integrates prior work on product, process, and resources (PPR) as well as cause-effect modeling. To identify possible attack chains and their impact on safety issues, we leverage Bayesian Belief Networks to estimate failure probabilities and propagate them through the knowledge graph. We evaluate our approach by means of a real-world manufacturing use-case.