Computational and Behavioral Trust Assurance by Utilizing Profile-based Risk Assessments: The CATM Methodology
Bibliographic record
Abstract
Communication within a distributed system may be abstracted as an interaction of two endpoints of communications that traverse through intermediary nodes. With the explosion of new applications and services in the Internet as well as the new capabilities of the sensor-based and data-driven services, that are described as the Internet of Things (IoT), a major requirement arises that should facilitate trust between endpoints of communication. Security issues arise due to occurrence of incidents that compromise computational and behavioral trusts. In distributed systems endpoints of communications might consume or provide services as well as exchange messages between senders and recipients. A major issue in all types of interactions is to convey trust between any two points of communication. These systems are deployed based on different architectures. Within the Internet systems require assurance prior communications processes occur. This research introduce a trust management approach that can be utilized by any node that communication within a distributed system. The methodology utilizes a profile-based approach to achieve high level of assurance process that can achieve any security requirement including confidentiality, availability, authenticity, integrity, and non-repudiation. It allows the abstraction and inclusion of different attributes of both computational and behavioral trusts. The approach is extensible in nature, where modular security requirements are added as needed. The methodology can be utilized as a gatekeeper and as an access control mechanism. The methodology is an application layer solution of the OSI model that defines five building blocks: profile definition, profile abstraction, profile exchange, profile verification, and trust evaluation. The methodology requires extensible implementation in order to guarantee interoperability.
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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 | 0.012 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".