False Data Injection Attacks Against State Estimation in Power Distribution Systems
Bibliographic record
Abstract
The existing research on false data injection (FDI) attacks against state estimation in transmission systems cannot be trivially extended to distribution feeders. The main reason is that a strong condition that requires the attacker to know the estimated state of distribution systems is needed, which makes the traditional FDI attacks difficult to be implemented in practice. In this paper, we propose a practical FDI attack model against state estimation in distribution systems, without paying expensive cost for obtaining the system state. We show that the attacker can approximate the system state based on power flow or injection measurements without too much effort. For local FDI attacks, the strong condition can be further relaxed to the knowledge of local state, which can be approximated based on a small number of power flow or injection measurements. Simulation results based on the IEEE test feeder demonstrate that the proposed practical FDI attack, even with the approximated system state, is more likely to compromise the state estimation without being detected, in comparison with the traditional attacks. This paper provides a basis to study the attack behaviors in distribution systems and a theoretical guide to develop protective countermeasures.
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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 | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".