Optimal Coding Schemes for Detecting False Data Injection Attacks in Power System State Estimation
Bibliographic record
Abstract
As a side-effect-free method, meter coding can successfully detect stealthy false data injection (FDI) attacks without significantly affecting physical plants by encoding the sensor outputs with an invertible matrix. However, since the relationship between the detection of stealthy FDI attacks and the cost of meter coding was not clearly analyzed in existing works, the optimal design of coding schemes has not been well studied. This paper investigates the optimal design of coding schemes based on the analysis of detection conditions for stealthy FDI attacks in power system state estimation. Specifically, detection conditions for stealthy FDI attacks are derived in both general and special coding schemes, which reveal the requirements on the encoded measurements, the coding scheme and the meters in power systems. Utilizing the constraints determined by the detection conditions, the optimization of the special coding scheme is formulated and equivalently simplified, which decouples the design of coding matrix with the other decision variables. Finally, simulation results are carried out on the alternating current (AC) state estimation in the IEEE 14-bus and 57-bus test systems to validate theoretical results on the conditions and cost of detecting stealthy FDI attacks in meter coding.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| 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 teacher head, 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".