An Imputation-based Augmented Anomaly Detection from Large Traces of Operating System Events
Bibliographic record
Abstract
Software debugging, audit, and compliance testing are some of the tasks we perform using execution traces of an operating system. However, these actions gather information about the behavior of the software vis-a-vis its design aims. In this work, our analysis of the execution traces of an embedded real-time operating system (RTOS) is rather to model the behavior of the physical system being managed by the software application via the embedded operating system. Hence, for an event-triggered embedded RTOS that controls the behavior of a bespoke system like an unmanned aerial vehicle (UAV), the events in the execution traces of the embedded RTOS is directly linked to the operation of the controlled physical system. Therefore, we hypothesize that the frequency of events (method/function calls) per observation is a useful feature for modeling the behavior of the physical system controlled by the operating system.
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How this classification was reachedexpand
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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".