Mixed strategy for power grid resilience enhancement under cyberattack
Bibliographic record
Abstract
Power infrastructure networks continue to be at risk under natural and anthropogenic hazard events. Cyberattacks targeting power grids aim at magnifying the impacts through damage propagation to other dependent infrastructure network. In this respect, the current study focuses on the “draw-down” phase of power infrastructure network resilience considering different centrality measures to evaluate the robustness of power grids against cyberattacks. The study considers two network representations for the grid based on the network’s topological/connectivity (i.e., unweighted network) and the network’s power flow data (i.e., weighted network). Therefore, the study utilizes the evaluated measures to improve network robustness under cyberattacks, through considering (or not) the proposed mixed strategy. Based on the analyses, network-level vulnerability is quantified considering five different scenarios through evaluating two key performance metrics—Topology and Functionality indices. Nonetheless, applying the proposed mixed strategy to limit the attacker’s access to the network hubs would boost the overall grid robustness.
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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 | 0.002 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".