An Overview of the Cyber Resiliency Level® (CRL®) Framework for Weapon, Mission, and Training Systems
Bibliographic record
Abstract
View Video Presentation: https://doi.org/10.2514/6.2021-0659.vid The deficiency in risk awareness, coupled with rapid technology changes within complex environments that are continuously under attack, make measuring cyber resiliency a hard problem. Lockheed Martin (LM) Fellows and cybersecurity subject matter experts from across the corporation developed and piloted the Cyber Resiliency Level® (CRL™) Framework as a standard way to measure the cyber resiliency maturity of weapon, mission, and/or training systems. The CRL™ Framework can be used to assist stakeholders in prioritizing risks and selecting courses of action for maximum effect against cyber-attacks; and, provides stakeholders with an understanding of cyber investments necessary for increased cyber resilience.
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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 | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.014 | 0.013 |
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".