Assessing the Vulnerability of Power Systems to False Data Injection Attacks: A Bayesian Attack Graph-Based Approach
Bibliographic record
Abstract
A meticulously crafted false data injection attack (FDIA) can effectively circumvent bad data detection mechanisms within the state estimation scheme, presenting falsified states to operators. These falsified states can lead to wrong operation decisions, potentially leading to line overloads and triggering cascading failures. Although substantial measurements are needed for executing such impactful FDIAs, the modeling of the cyber layer in vulnerability analysis of power grids is often neglected in the literature. Building on this foundation, this paper introduces a two-step vulnerability assessment approach that integrates the cyber layer into account. Initially, the minimum sets of phasor measurement units necessary to stealthily overload a transmission line is determined via a bi-level optimization problem for each line. Subsequently, Bayesian attack graphs are developed for each set to map all potential access routes for these minimal measurement sets, thereby facilitating the calculation of their accessing probability, which reflect the current vulnerability of the power system to FDIAs. The proposed methodology is tested and validated on the IEEE 39-Bus test system.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".