Detection of Load-Altering Cyberattacks Targeting Peak Shaving Using Residential Electric Water Heaters
Bibliographic record
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".