A Blockchain-based Dual Identity Management and Authentication Framework for IoT Networks
Bibliographic record
Abstract
The widespread adoption of Internet of Things (IoT) devices has introduced an era of unparalleled connectivity and data-driven innovation. Nevertheless, this rapid proliferation introduced significant security challenges. The limited computational capabilities of IoT devices impose constraints on the implementation of robust security mechanisms, rendering them susceptible to malicious attacks. Additionally, the dynamic nature of IoT systems which allows multiple administrators to add or remove IoT devices, necessitates the establishment of an identity management system. This system is crucial for managing both devices and system users, ensuring the traceability of device registration activities, and maintaining accountability of system users. Furthermore, traditional centralized authentication systems encounter scalability issues and high costs associated with centralized servers. To address these challenges, this paper proposes a Dual Identity Management and Authentication (DIMA) framework, leveraging blockchain technology. The proposed framework addresses two primary issues. Firstly, it assigns dual identities to each IoT node, facilitating a secure authentication process and regulating network access. Secondly, it supports the traceability of system device registration activities, enabling accountability for system users and ensuring a secure administrator-in-the-loop device identification process. Moreover, experimental testing and security analysis are undertaken to validate the usability and ascertain the critical security strengths of the DIMA framework.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".