Detection of Load-Altering Cyberattacks Targeting Peak Shaving Using Residential Electric Water Heaters

El-Nasser S. Youssef (), Fabrice Labeau and Marthe Kassouf
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El-Nasser S. Youssef: Department of Electrical and Computer Engineering, McGill University, Montreal, QC H3A 0G4, Canada
Fabrice Labeau: Department of Electrical and Computer Engineering, McGill University, Montreal, QC H3A 0G4, Canada
Marthe Kassouf: System Resiliency Unit, Hydro-Québec’s Research Institute, Varennes, QC J3X 1S1, Canada

Energies, 2022, vol. 15, issue 20, 1-19

Abstract: The rapid adoption of the smart grid’s nascent load-management capabilities, such as demand-side management and smart home systems, and the emergence of new classes of controllable high-wattage loads, such as energy storage systems and electric vehicles, magnify the smart grid’s exposure to load-altering cyberattacks. These attacks aim at disrupting power grid services by staging a synchronized activation/deactivation of numerous customers’ high-wattage appliances. A proper defense plan is needed to respond to such attacks and maintain the stability of the grid, and would include prevention, detection, mitigation, incident response, and/or recovery strategies. In this paper, we propose a solution to detect load-altering cyberattacks using a time-delay neural network that monitors the grid’s load profile. As a case study, we consider a cyberattack scenario against demand-side management programs that control the loads of residential electrical water heaters in order to perform peak shaving. The proposed solution can be adapted to other load-altering attacks involving different demand-side management programs or other classes of loads. Experiments verify the proposed solution’s efficacy in detecting load-altering attacks with high precision and low false alarm and latency.

Keywords: smart grid; cybersecurity; demand-side management; peak shaving; load-altering attacks; detection; time-delay neural networks (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
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