Trust Cyber Physical Systems: Trust Degree Framework and Evaluation
Bibliographic record
Abstract
Trust is the currency of every transaction and exchange and is the pillar of the trusted system concept, which is one of the needs of today and the future.Those systems ease communication and sharing with little user-side load and are used in numerous organizations, financial institutions, military scenarios, and highly confidential works.The evolution of Cyber-Physical Systems (CPS) affects people's way of life and is applied in health care, smart homes, commerce, etc.The cyber-physical system is treated as a trust system if the principles of security and safety, confidentiality, integrity, availability, and another set of properties are guaranteed.The development of CPS requires consistency in requirements management, metrics, formal test process descriptions, and computation methods.The research community is focused on how to realize a novel CPS with high confidence.In the literature, there is no clear definition of all kinds of trustworthiness metrics, and there is no classification of trustworthiness and trust metric types.There is no defined standard for trustworthiness, and there are no rules for calculating CPS trustworthiness.This paper proposes a framework for the evaluation of trust in CPS.This framework ranks the trustworthiness of CPS by degree.Trust degrees for the cyberphysical system are defined, along with a set of requirements and properties for each degree.We proposed a proprieties classification based on functionality and obligation as well as a simple mathematical formula to compute trust in the CPS, which formulates a quantitative view on the guarantee of security, trustworthiness, and trust attributes.The results of this study, which are based on the use of the proposed framework to evaluate the trust of CPS and case study, indicate that our quantitative method is more objective than existing qualitative methods.
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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 | 0.011 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".