Data-Driven Approximation of Formal Implicit Interaction Analysis for Cyber-Physical System Designs
Bibliographic record
Abstract
Implicit interactions are a type of system vulnerability which refer to sequences of communication between components in a system that are unplanned, unexpected, and/or unforeseen by system designers. The presence of implicit interactions can have impacts on system safety, security, stability, and resilience. The existing work to identify and analyze implicit interactions, as well as locate areas in a system for redesign to mitigate implicit interactions, all require the use of formal methods, which limits industry adoption. In this work, we identify measurements based on graph abstractions of system design models that can be used to produce similar results as the aforementioned formal analyses. We demonstrate this on a model of a real-world wastewater dechlorination system. By combining data collected from formal and graph measurements, we provide alternate methods to evaluate the prevalence of implicit interactions within a system design that can make security-by-design more accessible and widely adopted for more secure and resilient cyber-physical systems.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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".