Military Standard 1553B (MIL-STD-1553B) device classification: A comparative study
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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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".