Securing Non-Human Identities in Industrial IoT a Blockchain-Based Trust Framework
Bibliographic record
Abstract
The rapid growth of the Industrial Internet of Things (IIoT) has created a complex and often vulnerable landscape for managing non-human identities (NHIs) such as sensors, bots, and service accounts. In response, this research proposes a blockchain-based trust framework designed to move beyond traditional static security scoring models. By integrating decentralized identity management, lightweight consensus mechanisms, and smart contract enforcement, the framework provides a more dynamic and context-aware approach to assessing trust in IIoT environments. A key innovation of this model is the introduction of two metrics: the Security Confidence Score (CS), which combines identity assurance, blockchain integrity, and residual risk; and the Non-Human Identity Trust Score (Tₙₕᵢ), which evaluates trust at the device level based on authentication strength, behavioral patterns, and anomaly detection. The framework is validated through simulations across multiple Operational Technology (OT) environments in the oil and gas sector, demonstrating its practical applicability in reducing residual cybersecurity risk while strengthening overall identity trust. Its hybrid blockchain architecture, combining public and private layers, ensures both operational privacy and regulatory transparency. Proof-of-concept testing with sample datasets confirms the framework’s adaptability and context-aware capabilities, establishing it as a significant step forward in securing IIoT ecosystems.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".