The state of the art in critical infrastructure protection: a framework for convergence
Bibliographic record
Abstract
The protection of critical infrastructure systems has recently become a major concern for many countries. This is due to the effect of these systems on the daily lives of all citizens and the high possibility of disruption because of their complex structure and hidden interdependencies, which subsequently attract the attention of many researchers and scientists. The investigations of researchers have encompassed issues of national security, policymaking, infrastructure system organisation, and behaviour analysis and modelling. In this paper, we look into the latter subject and explore the attempts that have been made. Based on the available schemes and the requirements of this area, we propose a five-dimensional framework that introduces the major research necessities in this field. Among the various available schemes, we study ten of the most recently developed and/or influential systems. A comparison of these schemes based on the features of our proposed framework is made. The comparison allows us to conclude our examination with the identification of current research strengths and guidelines for future work.
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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 | 0.021 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.006 | 0.034 |
| Scholarly communication | 0.017 | 0.029 |
| Open science | 0.005 | 0.012 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.008 | 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".