Special Session: Physics- Informed Neural Networks for Securing Water Distribution Systems

Solon Falas, Charalambos Konstantinou, Maria K. Michael

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Physics-informed neural networks (PINNs) is an emerging category of neural networks which can be trained to solve supervised learning tasks while taking into consideration given laws of physics described by general nonlinear partial differential equations. PINNs demonstrate promising characteristics such as performance and accuracy using minimal amount of data for training, utilized to accurately represent the physical properties of a system's dynamic environment. In this work, we employ the emerging paradigm of PINNs to demonstrate their potential in enhancing the security of intelligent cyberphysical systems. In particular, we present a proof-of-concept scenario using the use case of water distribution networks, which involves an attack on a controller in charge of regulating a liquid pump through liquid flow sensor measurements. PINNs are used to mitigate the effects of the attack while demonstrating the applicability and challenges of the approach.
Original languageEnglish (US)
Title of host publicationProceedings - IEEE International Conference on Computer Design: VLSI in Computers and Processors
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages37-40
Number of pages4
ISBN (Print)9781728197104
DOIs
StatePublished - Oct 1 2020
Externally publishedYes

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