DACIoT: Dynamic Access Control Framework for IoT Deployments
Bibliographic record
Abstract
This article presents a dynamic access control framework for the Internet of Things (DACIoT). The main objective of DACIoT is to prevent unauthorized access to IoT devices and tightens the authorized access while an IoT device is in use. The rigidness of existing access control (AC) techniques in terms of manual policy specification, discontinuity of access decision making, and immutability to changing access behaviors makes these solutions fall short in highly dynamic IoT environments. DACIoT supports three functionalities that are lacking in existing AC solutions: 1) automatic policy generation; 2) continuous policy enforcement; and 3) adaptive policy adjustment. The DACIoT extends the standard reference model of the extensible AC markup language (XACML) with the added three functionalities to improve the adaptability of attribute-based AC policies to highly dynamic IoT environments. Results show that DACIoT provides improved security, dynamic adaptability, and can scale efficiently to IoT environments.
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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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| 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".