PulseAnomaly: Unsupervised Anomaly Detection on Avionic Platforms With Seasonality and Trend Modeling in Transformer Networks
Bibliographic record
Abstract
For communication within military avionic platforms (e.g., F-15 and F-35), the US Department of Defense established MIL-STD-1553 military standard. It has been released for more than 50 years and is still used in platforms other than military avionics. It was originally produced to be used with military avionics, but in the following decades, it was adopted into all branches of the armed forces, as well as spacecraft and commercial avionics. However, potential attacks against the MIL-STD-1553 may exist due to the demand for internet communication between planes and the lack of security. The current study presentsPulseAnomaly, a novel unsupervised anomaly detection model for the MIL-STD-1553 bus that utilizes time-feature and message sequences. Our model demonstrates better performance compared to baseline models in the test, achieving a higher F1-score and showing excellent AUROC compared to existing methods. Additionally, we have used data from a recently developed open-source MIL-STD-1553 real-time bus simulator, which features a more diverse range of attacks and data points that more closely resemble real-world scenarios. Evaluation results show that our model outperforms existing unsupervised solutions.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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 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".