SATI: Sidechain-Based Access Control & Trust Mechanism for IoT Networks
Bibliographic record
Abstract
Providing low latency, high security, and high resource utilization for Internet of Things (IoT) networks is challenging due to the heterogeneous nature of these networks and the need for more standardization in security algorithms. Current edge computing-based IoT solutions decrease network latency and improve resource utilization but do not provide adequate security because they offer multiple attack surfaces for adversaries. Recent work uses blockchain technology to provide better security in IoT networks. However, blockchain-based solutions suffer from scalability problems and can increase latency. Sidechains are parallel blockchain networks typically used to increase the scalability of blockchain networks. We propose a novel Sidechain-based Access control and Trust evaluation mechanism for IoT networks (SATI) to decrease network latency and improve scalability, security, and energy efficiency. SATI uses a sidechain with the blockchain network to improve its scalability. It also uses edge computing to provide low network latency and high resource utilization in terms of CPU and memory usage. In addition, trust evaluation and attribute-based access control mechanisms are used to improve the security of the IoT network. We compare our work with existing mechanisms in terms of scalability, security, latency, and CPU and memory usage. In addition, we perform a formal security analysis of the SATI mechanism using reduction-based analysis and the Scyther verification tool.
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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 | 0.004 | 0.007 |
| 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 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".