Project detail

Anomaly detection in critical infrastructures using machine learning

Duration: 01.01.2023 — 31.12.2025

Funding resources

Ministerstvo vnitra ČR - 1 VS OPSEC

- whole funder (2023-01-02 - 2029-07-31)

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

Horváth Tomáš, doc. Ing., Ph.D. - fellow researcher
Münster Petr, doc. Ing., Ph.D. - principal person responsible

Units

Department of Telecommunications
- beneficiary (2023-01-01 - 2025-12-31)

Results

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.