A Cyber-Physical Resilience-Based Survivability Metric against Topological Cyberattacks
Bibliographic record
Abstract
Recent cyberattacks targeting critical energy infrastructures illustrate the significant importance of resiliency during survive, sustain, and recovery phases of the underlying system. Motivated by this observation, the paper aims at introducing a quantitative framework to measure the survivability of Cyber-Physical Systems (CPSs) against systematic cyberattacks targeting the power grid topology. In the proposed Cyber-Physical Resilience-based Survivability Metric (CP-RSM), the concept of Survivability Margin (SM) is introduced to observe the system’s ability in preserving the functionality of its crucial components. Available Generation (AG) and Network Accordant Connectivity (NAC) are taken into consideration to measure Power-side Survivability (PsS). Moreover, Cyber-side Survivability (CsS) is quantified based on the ultimate potential damage to the power grid based on alerts received from different security devices. Using the proposed metric, the system operator can perform corrective actions such as unit re-dispatching or system reconfiguration to minimize the damage. Effectiveness of the proposed CP-RSM is evaluated based on the PJM 5-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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".