Network Parameter Coordinated False Data Injection Attacks Against Power System AC State Estimation
Bibliographic record
Abstract
False data injection (FDI) attacks have recently been introduced as an important class of cyber-attacks against power system state estimation. Utilizing vulnerabilities in information systems, attackers can inject well-constructed false data and stealthily misguide results of state estimation. However, only measurements, such as power flows and bus injections, are coordinately modified in FDI attacks, which may result in a larger number of modified measurements in constructing such attacks. In this article, we propose a network parameter coordinated false data injection (NP-FDI) attack to reduce the number of attacked measurements, where expected changes of system states and modifications of network parameters are well coordinated. Analysis of minimal attack set at a single line gives feasible conditions in reducing the number of attacked measurements. A sparse attack strategy is designed to obtain the minimal attack set of the whole grid, which can also be applied to cases with incomplete topology information. An extension to NP-FDI attacks with incomplete line impedance is presented, where the required line impedance can be estimated from local measurements adjacent to the targeted branch. Based on simulations in the IEEE 14-bus and IEEE 118-bus test systems, performance of the proposed NP-FDI attacks on sparsity and stealth are evaluated.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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 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".