PEACE: Physics-Enabled Autoencoder Detection and Localization of Load-Altering Attacks in Smart Grids
Bibliographic record
Abstract
The introduction and incorporation of smart technologies and IoT loads into the traditional power grid has transformed it into a more sustainable smart grid. However, this shift has exposed the grid to a wide array of threats initiated through its new cyber layer. Load-Altering Attacks (LAAs) are one such family of threats that have devastating impacts on the grid“s stability. In this paper, we present PEACE, a Physics-Enabled Autoencoder LAA detection and localization scheme. To this end, the fluctuations in the loads on the individual buses are estimated by processing the collected generator frequencies through the mathematical model representing the grid“s physical behavior. These fluctuations are then fed into an Autoencoder (AE) to detect the presence of attacks. The proposed mechanism achieves 99.6% accuracy even when tested on a power grid it was never trained on. Additionally, the AE demonstrates robustness to data contamination during training.
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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 | 0.001 | 0.002 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".