Formal Model-Based Traceability for Security Compliance in Satellite Control Systems
Bibliographic record
Abstract
Ensuring robust security compliance and traceability is a critical challenge for Low-Earth Orbit (LEO) satellite control systems, given the complex landscape of evolving standards and regulatory requirements throughout their development lifecycle. This paper addresses the challenge of maintaining security compliance and traceability in LEO satellite control systems, which must adhere to multiple standards and regulations throughout the system development lifecycle. Existing work focuses on space system resilience but lacks comprehensive methods for compliance traceability. To fill this gap, we adopt a formal model-based systems engineering (MBSE) framework, previously applied for Supervisory Control and Data Acquisition (SCADA) systems, to support compliance checking for LEO satellite control systems. Using a domain-specific language, we model the software architecture with security controls and compliance requirements from standards like NIST SP 800-53 and CNSS Policy 12. The model is automatically translated into an Alloy formal model for automated compliance analysis. The results show that the framework not only enhances compliance traceability but also effectively identifies discrepancies and recommends appropriate controls. The reusable nature of the framework offers broader applications in aviation and critical infrastructure, streamlining assurance and audit processes.
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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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| 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".