TABI: Trust-Based ABAC Mechanism for Edge-IoT Using Blockchain Technology
Bibliographic record
Abstract
Recent research has focused on applying blockchain technology to solve security-related problems in Internet of Things (IoT) networks. However, implementing blockchain technology directly on IoT networks is prone to high overheads and energy-expensive operations. Therefore, in this paper, we use edge computing technology to avoid these problems. We also propose a novel Trust-based Access Control Mechanism for Edge-IoT Networks using Blockchain technology (named TABI) to implement end-to-end security in resource-constrained IoT networks. The TABI mechanism utilizes both access control and trust evaluation mechanisms to mitigate the impact of malicious IoT users and devices. Additionally, it incorporates permissioned Hyperledger blockchain technology to provide an added layer of security through authentication. The trust evaluation mechanism is implemented as a trust calculation contract (TCC) on the edge devices using Hyperledger Composer. The access control mechanism employs an Attribute-based Access Control (ABAC) mechanism, which is implemented on the Hyperledger blockchain using two smart contracts: the attribute contract (AC) and the access control contract (ACC). We implement a proof-of-concept (PoC) implementation using Hyperledger Caliper (a benchmark testing tool) and Docker images. Our evaluation includes five analyses: Trust Evaluation Mechanism, Access Control Mechanism, Security, Blockchain, and IoT Applications. Through this evaluation, we highlight the effectiveness of TABI in terms of throughput, latency, detection of malicious IoT devices, and resource consumption of the IoT devices. Our analyses demonstrate that TABI is particularly useful in IoT applications that require low latency and resource efficiency.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".