DynPolAC: Dynamic Policy-Based Access Control for IoT Systems
Why this work is in the frame
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Bibliographic record
Abstract
In the near future, Internet-of-Things (IoT) systems will be comprised of autonomous, highly interactive and moving objects that require frequent handshakes to exchange information in time intervals of seconds. Examples of such systems are drones and self-driving cars. In these scenarios, data integrity, confidentiality, and privacy protection are of critical importance. Further, updates need to be processed quickly and with low overheads due to the systems' resource-constrained nature. This paper proposes Dynamic Policy-based Access Control (DynPolAC) as a model for protecting information in such systems. We construct a new access control policy language that satisfies the properties of highly dynamic IoT environments. Our access control engine is comprised of a rule parser and a checker to process policies and update them at run-time with minimum service disruption. DynPolAC achieves more than 7x performance improvements when compared to previously proposed methods for authorization on resource-constrained IoT platforms, and achieves more than 3x faster response times overall.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it