DynPolAC: Dynamic Policy-Based Access Control for IoT Systems
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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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 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".