Principles and Properties for Reducing the Prevalence of Implicit Interactions in System Designs.
Bibliographic record
Abstract
Early security considerations are essential to ensuring a system is adequately protected, but their ever-growing size and complexity often leaves full comprehension of a system's interconnections out of reach.This gives rise to implicit interactions.These unplanned or unforeseen communication sequences between components are security vulnerabilities that can be exploited to mount a cyberattack.Existing design-phase formal methods-based approaches exist to identify implicit interactions, but formal methods see limited adoption and the root cause of implicit interactions is not well understood.In this work, we extend the existing formal approach to suggest areas of a system to focus redesign efforts, while also providing alternative approaches that do not require formal expertise.These focus on graph-based measurements and providing a set of properties, quality attributes, and design principles with goals in line with the reduction of the prevalence of implicit interactions within a system design. List of Figures1.1 A Simple Example System . . . . . . . . . . .
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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 | 0.028 | 0.108 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.014 |
| Scholarly communication | 0.005 | 0.015 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.004 | 0.012 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".