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Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Chance-constrained vulnerability assessment of smart water distribution systems against stealthy false data injection attacks
https://orcid.org/0009-0002-5433-8720
https://orcid.org/0000-0002-3178-2055
Abstract Smart water distribution systems (SWDSs) have not only enhanced the monitoring and control of the entire water infrastructure but have also jeopardized its security and resilience. The state-of-the-art examined the uncertainties in the water supply network to obtain optimal design parameters to ensure hydraulic integrity. However, the investigations of uncertainties in SWDSs from the cybersecurity perspective remain unexplored. To address this limitation, this paper utilizes a robust chance-constrained optimization strategy to scrutinize the vulnerable location of SWDSs against False Data Injection (FDI) attacks. This is achieved by factoring in the probabilistic behavior of water demand at junctions that can potentially facilitate stealthy FDI attacks on the hourly measurements of the pump’s flow rate. The proposed nonlinear model is validated with several case studies reflecting the real-world cyberattack consequences including cutoff water supply of the network, enhanced power consumption of the pump, and the undesirable pressure surge in the system at strategic locations of SWDSs. As a result of the studied cases, the cyberattack consequences on the entire water supply network are obtained while highlighting the threat-prone regions of the network. This paper contributes to embedding additional security layers to the existing water infrastructure at the vulnerable links and junctions.
Graphical abstract Display Omitted
Highlights Vulnerability of water systems against false data injection attacks is studied. Uncertainty in water demand at junctions is modeled as chance-constraints. Validation is obtained by case studies with real-world attack consequences. The proposed model will enhance the cybersecurity aspects of water systems.
Chance-constrained vulnerability assessment of smart water distribution systems against stealthy false data injection attacks
https://orcid.org/0009-0002-5433-8720
https://orcid.org/0000-0002-3178-2055
Abstract Smart water distribution systems (SWDSs) have not only enhanced the monitoring and control of the entire water infrastructure but have also jeopardized its security and resilience. The state-of-the-art examined the uncertainties in the water supply network to obtain optimal design parameters to ensure hydraulic integrity. However, the investigations of uncertainties in SWDSs from the cybersecurity perspective remain unexplored. To address this limitation, this paper utilizes a robust chance-constrained optimization strategy to scrutinize the vulnerable location of SWDSs against False Data Injection (FDI) attacks. This is achieved by factoring in the probabilistic behavior of water demand at junctions that can potentially facilitate stealthy FDI attacks on the hourly measurements of the pump’s flow rate. The proposed nonlinear model is validated with several case studies reflecting the real-world cyberattack consequences including cutoff water supply of the network, enhanced power consumption of the pump, and the undesirable pressure surge in the system at strategic locations of SWDSs. As a result of the studied cases, the cyberattack consequences on the entire water supply network are obtained while highlighting the threat-prone regions of the network. This paper contributes to embedding additional security layers to the existing water infrastructure at the vulnerable links and junctions.
Graphical abstract Display Omitted
Highlights Vulnerability of water systems against false data injection attacks is studied. Uncertainty in water demand at junctions is modeled as chance-constraints. Validation is obtained by case studies with real-world attack consequences. The proposed model will enhance the cybersecurity aspects of water systems.
Chance-constrained vulnerability assessment of smart water distribution systems against stealthy false data injection attacks
https://orcid.org/0009-0002-5433-8720
https://orcid.org/0000-0002-3178-2055
Abstract Smart water distribution systems (SWDSs) have not only enhanced the monitoring and control of the entire water infrastructure but have also jeopardized its security and resilience. The state-of-the-art examined the uncertainties in the water supply network to obtain optimal design parameters to ensure hydraulic integrity. However, the investigations of uncertainties in SWDSs from the cybersecurity perspective remain unexplored. To address this limitation, this paper utilizes a robust chance-constrained optimization strategy to scrutinize the vulnerable location of SWDSs against False Data Injection (FDI) attacks. This is achieved by factoring in the probabilistic behavior of water demand at junctions that can potentially facilitate stealthy FDI attacks on the hourly measurements of the pump’s flow rate. The proposed nonlinear model is validated with several case studies reflecting the real-world cyberattack consequences including cutoff water supply of the network, enhanced power consumption of the pump, and the undesirable pressure surge in the system at strategic locations of SWDSs. As a result of the studied cases, the cyberattack consequences on the entire water supply network are obtained while highlighting the threat-prone regions of the network. This paper contributes to embedding additional security layers to the existing water infrastructure at the vulnerable links and junctions.
Graphical abstract Display Omitted
Highlights Vulnerability of water systems against false data injection attacks is studied. Uncertainty in water demand at junctions is modeled as chance-constraints. Validation is obtained by case studies with real-world attack consequences. The proposed model will enhance the cybersecurity aspects of water systems.
Chance-constrained vulnerability assessment of smart water distribution systems against stealthy false data injection attacks
Raza, Nazia (Autor:in) / Moazeni, Faegheh (Autor:in)
28.11.2023
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
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