Analyzing Implicit Interactions to Identify Weak Points in Cyber-Physical System Designs
Bibliographic record
Abstract
Cyber-physical systems often consist of many interacting components with numerous communication paths, some of which may be unintended and/or unforeseen by system designers. The existence of such paths, known as implicit interactions, represent security vulnerabilities that can be exploited to mount cyberattacks and destabilize a system. For any system with an abundance of implicit interactions, it can be difficult to understand which aspects of the design contribute most to the presence of this vulnerability. In this paper, we present an approach to identify weak points in the designs of cyber-physical systems based on frequency analyses of the interactions, both implicit and intended, present in a system design. We demonstrate the approach with a real-world Wastewater Dechlorination System. The proposed method can aid system designers in understanding which aspects of their systems contribute most to the existence of implicit interactions to enable effective mitigation strategies to improve overall system security and resilience.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".