Using a System-Theoretic Approach for Cyber Mission Assurance of the Royal Canadian Air Force Over the Horizon Radar System
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it