Using a System-Theoretic Approach for Cyber Mission Assurance of the Royal Canadian Air Force Over the Horizon Radar System
Bibliographic record
Abstract
Since 1958, the North American Aerospace Defence between Canada and the United States remains as the only bi-national military command in the world. Among many of its responsibilities, the need for early detection of threats against the North American aerospace demands improved visibility in terms of both range and coverage over the Northern Canadian Area of Responsibility. However, the existing fleet of radar systems are not only limited but fast approaching technological obsolescence against modern adversarial weapon systems. As a solution, the Royal Canadian Air Force committed to deliver the Over the Horizon Radar systems that will significantly enhance the existing NORAD capabilities in detecting adversarial northern approaches. The Royal Canadian Air Force conducts Cyber Mission Assurance on its future weapon systems. Hence understanding of cyber vulnerabilities permeating the Over the Horizon Radar systems is a mandatory exercise that must take place concurrent to the Project Management and acquisition efforts. Considering this, a novel methodology known as the STPA-Sec is employed to conduct Cyber Mission Assurance of the Over the Horizon Radar systems. Contrary to the traditional methods to manage cyber risks, the STPA-Sec defines the scope of the system, illustrates the attack surface, as well, offers a set of operational constraints within which, if complied, minimizes risks of defined system failures. The application of STPA-Sec on the Over the Horizon Radar systems yields a concrete set of recommendations that, if followed, will minimize systemic and multi-faceted risks that are otherwise unconceivable using the traditional methods.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".