@article{06530971370f4a9e861d284c886101ff,
title = "A time-series self-supervised learning approach to detection of cyber-physical attacks in water distribution systems",
keywords = "Attack detection, Data intelligence, Industrial cyber-physical systems, Self-supervised learning, Water distribution system",
author = "Haitham Mahmoud and Wenyan Wu and Mohamed Gaber",
note = "Funding Information: Funding: This research has received funding from the European Union{\textquoteright}s Horizon 2020 research and innovation program under the Marie Sklodowska-Curie Training Networks (ITN)-IoT4Win grant agreement No. [765921]. Funding Information: Acknowledgments: This research has received funding from the European Union{\textquoteright}s Horizon 2020 research and innovation program under the Marie Sk{\l}odowska-Curie Training Networks (ITN)-IoT4Win grant agreement No. [765921]. Publisher Copyright: {\textcopyright} 2022 by the authors. Licensee MDPI, Basel, Switzerland.; European Union{\textquoteright}s Horizon 2020 research and innovation program under the Marie Sklodowska-Curie Training Networks (ITN)-IoT4Win grant agreement No. [765921]. ; Conference date: 01-09-2018",
year = "2022",
month = jan,
day = "27",
doi = "10.3390/en15030914",
language = "English",
volume = "15",
pages = "914",
journal = "Energies",
issn = "1996-1073",
publisher = "MDPI",
number = "3",
}