Project detail
Anomaly detection in critical infrastructures using machine learning
Duration: 1.1.2023 — 31.12.2025
Funding resources
Ministerstvo vnitra ČR - 1 VS OPSEC
On the project
Description in English
The project aims to design and realization of a laboratory testbed that enables from the transmission parameters of data signals at transmission speeds ranging from 1 to 200 Gbit/s, events such as network degradation, cable manipulation or attacker detection. Testbed allows variability in configuration (different distances, combination of passive components, different modulation formats, etc.) and will be used to create datasets for machine learning to improve the security of optical fiber infrastructures. The results will serve the professional and general audience and will be shared in academic papers.
Key words in English
optical network, data communication, dataset, machine learning
Mark
VK01030048
Default language
Czech
People responsible
Münster Petr, doc. Ing., Ph.D. - principal person responsible
Horváth Tomáš, doc. Ing., Ph.D. - fellow researcher
Units
Department of Telecommunications
- responsible department (24.5.2022 - not assigned)
Department of Telecommunications
- beneficiary (1.1.2023 - 31.12.2025)
Results
HORVÁTH, T.; RADIL, J.; ŠÍMA, J. Interaction analysis: coherent vs. legacy transmission data systems for ITU grid spacing. In Proc. SPIE 13129, Optical Modeling and Performance Predictions XIV. San Diego, USA: SPIE, 2024. p. 1-4. ISBN: 9781510679184.
Detail
MÜNSTER, P.; TOMAŠOV, A.; DEJDAR, P.; HORVÁTH, T. Deploying Machine Learning in Distributed Sensing to Increase Resilience of Fiber Optic Infrastructure. In 2023 Conference on Lasers and Electro-Optics (CLEO). San Jose, CA, USA: Optica Publishing Group, 2023. ISBN: 978-1-957171-25-8.
Detail
Responsibility: Münster Petr, doc. Ing., Ph.D.