Efficient prevention technique for false data injection attack in smart grid
Bibliographic record
Abstract
False data injection (FDI) attack is one of the severe attacks that threatens the smart grid's efficiency and reliability. Power grid's status is monitored by many measurement units spread all over the grid; these units periodically send their measurements to the grid's control center (CC), which utilizes them to make the right decisions for the grid. FDI attack inserts fake measurements among the correct ones to mislead CC to make wrong decisions and consequently impact on the grid's performance. Several works are proposed to only detect FDI attack utilizing various estimation tests and optimization techniques. In our paper, we propose an FDI attack prevention technique that is based on protecting the integrity of the measurements at measurement units and during their transmission to the CC, even with the presence of compromised units. So, the proposed scheme alleviates the negative impacts of FDI attack on grid's performance. Security analysis and performance evaluation show that our scheme guarantees the integrity and availability of the measurements with lightweight overhead, especially on the restricted-capabilities measurement units.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".