Military Standard 1553B (MIL-STD-1553B) device classification: A comparative study
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
In 2017, Canada presented its intent to buy C$90 million worth of fighter jets. While these jets need regular modifications to be kept up to date with the airworthiness standard of Canada, they rely on older aircraft architecture like the MIL-STD-1553B. In this thesis, we investigate the MIL-STD-1553B technology used in aircraft systems and explore how aircraft components can be automatically classified. We propose a novel a two-step active scanning approach to establish message timing, built-in test responses and memory contents in order to classify the devices. OMAP is able to classify device types and versions. We compared the accuracy of multiple Machine Learning classification algorithms when exposed to different test case scenarios as well as compared: One-step vs Two-step classification, Joined vs Separate spoofed class, Timing features granularity effects. Finally, using ANN and SVC we obtained a classification accuracy of 95% for device type and 88% for device version.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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