DTW-ABAC: A Dynamic Trust Weighted Attribute-Based Access Control Hybrid Security Model for Cloud Applications
Bibliographic record
Abstract
Modern digital infrastructures require access control systems that protect sensitive data and adapt to evolving contexts and user behaviour. While traditional foundational models provide basic enforcement, they lack flexibility, granularity and real-time responsiveness. To bridge these gaps, this research proposes a hybrid framework that combines structured policy logic with dynamic evaluation capabilities. The framework contributes to three major advancements. First, it introduces a trust scoring and attribute-criticality mechanism that enables dynamic risk adaptation and optimizes performance through task division and short-circuit evaluation by combining conventional ABAC methods. Second, it proposes a multi-step scenario generation process to produce realistic and policy-relevant access scenarios. Third, it enhances decision precision, transparency, and platform independence through improved policy components, context handling, and RESTful architecture, ensuring scalability beyond vendor constraints. The framework leverages Microsoft Entra ID for consistent and secure identity and attribute management. Weighted attribute evaluation ensures policy flexibility, while scenario-driven testing and detailed audit logs increase transparency and accountability. Comparative analysis reveals that the hybrid model yields more accurate, adaptive, and explainable decisions than standalone XACML or NGAC, making it a strong candidate for enterprise and cloud-scale deployments where contextual nuance and high security reliability are crucial.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".