Adversarial Attacks on Machine Learning-Based State Estimation in Power Distribution Systems
Why this work is in the frame
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Bibliographic record
Abstract
We examine the robustness of machine learning-based distribution system state estimation (DSSE) techniques to a class of adversarial attacks, known as the black-box evasion attack. In these attacks, the attacker manipulates real-time measurements from sensors installed in the distribution grid by adding carefully crafted perturbations to diminish the accuracy of DSSE. We devise a stealthy attack based on the Fast Gradient Sign Method (FGSM), dubbed Sneaky-FGSM, by analyzing the statistical properties of real-time measurements and adding perturbations accordingly. Using simulation on a standard test distribution system, we show that this attack would remain largely unidentified and the error introduced in the DSSE process could propagate to a voltage control scheme that takes the DSSE result as input. Our result suggests that incorporating machine learning models in DSSE is a double-edged sword and calls for more research to ensure the robustness of these models to adversarial samples.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 it