Optimizing Data Aggregation in Power Systems to Defend Against False Data Injection Attacks
Bibliographic record
Abstract
The integration of information and communication technologies into power systems has introduced new cyber vulnerabilities, particularly stealthy false data injection attacks (SFDIAs) targeting state estimation. These attacks can evade bad data detection mechanisms, leading to inefficient dispatch, line overloads, or cascading outages. Although most existing defense strategies focus on securing individual PMUs, they often overlook vulnerabilities at phasor data concentrators (PDCs), which aggregate measurements from multiple correlated PMUs. This risk is especially pronounced in conventional geography-based PMU-to-PDC assignments, in which neighboring and highly correlated PMUs are grouped under the same PDC. On this basis, this paper first demonstrates, using Bayesian attack graphs, that compromising PDCs poses a greater risk than targeting individual PMUs. Then, to mitigate this vulnerability, a tri-level defender-attacker-operator optimization model is proposed to identify optimal PMU-to-PDC mappings that minimize attack impact. Simulation results highlight the importance of data aggregation architecture in system vulnerability and demonstrate that optimized allocations significantly improve resilience against high-impact SFDIAs.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| 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".