Modelling False Data Injection Attacks Against Non-linear State Estimation in AC Power Systems
Bibliographic record
Abstract
False data injection (FDI) attacks can disrupt the operation of the smart grid by manipulating the state estimation process without being detected. To deal with these kinds of attacks on smart grid networks, vulnerability analysis should carefully be developed under realistic conditions. Existing research efforts on FDI attacks have not performed the vulnerability analysis of the smart grid using the AC state estimation without incurring significant computational complexity. In this paper, we develop a low-complexity system to model the least-effort FDI attacks in the AC power grid. We do that by using a reduced row echelon (RRE) form-based greedy method on the AC state estimation process to compute the minimum number of measurements an attacker needs to compromise to launch the undetectable low-cost FDI attack with more efficiency. Simulation results obtained for various IEEE standard test systems show the efficient performance and enhanced accuracy of our proposed approach for modeling least-effort attacks.
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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".