A time-series self-supervised learning approach to detection of cyber-physical attacks in water distribution systems

Haitham Mahmoud; Wenyan Wu; Mohamed Gaber

doi:10.3390/en15030914

A time-series self-supervised learning approach to detection of cyber-physical attacks in water distribution systems

Haitham Mahmoud (Corresponding / Lead Author), Wenyan Wu, Mohamed Gaber

Research output: Contribution to journal › Article › peer-review

26 Citations (SciVal)

Original language	English
Article number	914
Pages (from-to)	914
Journal	Energies
Volume	15
Issue number	3
DOIs	https://doi.org/10.3390/en15030914
Publication status	Published (VoR) - 27 Jan 2022
Event	European Union’s Horizon 2020 research and innovation program under the Marie Sklodowska-Curie Training Networks (ITN)-IoT4Win grant agreement No. [765921]. - Duration: 1 Sept 2018 → …

Funding

Funding: This research has received funding from the European Union’s Horizon 2020 research and innovation program under the Marie Sklodowska-Curie Training Networks (ITN)-IoT4Win grant agreement No. [765921]. Acknowledgments: This research has received funding from the European Union’s Horizon 2020 research and innovation program under the Marie Skłodowska-Curie Training Networks (ITN)-IoT4Win grant agreement No. [765921].

Keywords

Attack detection
Data intelligence
Industrial cyber-physical systems
Self-supervised learning
Water distribution system

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10.3390/en15030914

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AU - Mahmoud, Haitham

AU - Wu, Wenyan

AU - Gaber, Mohamed

N1 - Funding Information: Funding: This research has received funding from the European Union’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’s Horizon 2020 research and innovation program under the Marie Skłodowska-Curie Training Networks (ITN)-IoT4Win grant agreement No. [765921]. Publisher Copyright: © 2022 by the authors. Licensee MDPI, Basel, Switzerland.

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KW - Attack detection

KW - Data intelligence

KW - Industrial cyber-physical systems

KW - Self-supervised learning

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A time-series self-supervised learning approach to detection of cyber-physical attacks in water distribution systems

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