A Security Compliance-by-Design Framework Utilizing Reusable Formal Models
Bibliographic record
Abstract
In recent years, increasing concerns about the security of critical infrastructure have led to the development of various security standards, policies, and regulations. Consequently, it has become essential for any organization responsible for such infrastructure to ensure that their system software architecture complies with these security guidelines. As a result, in this paper we propose a methodology to enhance the security of software systems by incorporating compliance verification from the early stages of design, thereby proactively addressing potential flaws. Furthermore, we present a novel method for modeling a security compliance baseline based on the specification and reuse of analysis models targeting standards, policies, and regulations. This approach streamlines the compliance process, facilitating adherence to multiple security standards while promoting the reuse of security compliance analysis models. To demonstrate the practicality of the suggested framework and technique, we illustrate representative architecture compliance checks on a Supervisory Control and Data Acquisition (SCADA) 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 | 0.027 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.004 | 0.006 |
| 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".