Methodological Resilience Assessment of Smart Cyber Infrastructures
Bibliographic record
Abstract
Abstract The race for digitization created a real need to protect smart infrastructures. Environments are becoming highly connected and automated. Their growing complexity and connectivity make it hard to assure and assess their cyber resilience, i.e., protecting them from cyberattacks, failures, and errors. Traditional strategies for ensuring the cyber resilience of smart infrastructures suffer from a lack of holism. Indeed, since smart infrastructures are often structured in layers, traditional protection methods can lead to conflicting and competing goals. For instance, they may increase the resilience of specific layers at the expense of decreasing the performance of others. This chapter reviews existing methods aiming to address this problem. We focus on two leading methodological assessment families: quantitative and qualitative. The former includes numerical metrics to quantify and assist system-dependent decision-making processes. The latter builds upon symbolic modeling to offer a system-agnostic assessment. The chapter provides an in-depth exploration of quantitative and qualitative methodologies with significant potential to enhance the resilience of layered smart infrastructures. Our exploration covers classical technological aspects (e.g., cascading effects) and socio-technical factors (e.g., human-in-the-loop interaction).
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".