Establishing and Maintaining Trust for an Airborne Network. Search and Rescue Enterprise: Security Assessment Report
Bibliographic record
Abstract
Abstract : This report was developed under a SBIR contract for topic AF103-165. This report describes the results a security assessment conducted on the Search and Rescue (SAR) enterprise. The purpose of the security assessment is to identify the operational risks to the SAR enterprise. In particular, those resulting from cyber attacks, identify the corresponding vulnerabilities, assess the criticality of the components, and recommend mitigations. SAR case study is a comprehensive illustration to the Department of Defense Architecture Framework (DoDAF) published as part of the international standard UML Profile for DoDAF and MoDAF (UPDM) by the Object Management Group (OMG). For the purposes of this assessment, the SAR is defined by International Aeronautical and Maritime Search and Rescue Manual (IAMSAR) and Canadian National Search and Rescue Manual. The security assessment described herein was one part of an overall project to develop a generic methodology and technology framework for computing a trustworthiness index (TI). A TI is a measure of confidence that risk is low in a claim about a system component supporting mission objectives
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.
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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".