<scp>LightAuth</scp> : A Lightweight Sensor Nodes Authentication Framework for Smart Health System
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
ABSTRACT Counterfeit medical devices pose a threat to patient safety, necessitating a secure device authentication system for medical applications. Resource‐constrained sensory nodes are vulnerable to hacking, prompting the need for robust security measures. Token‐based authentication schemes, such as one‐time passwords (OTPs), smart cards, key fobs, and mobile authentication apps, along with certificate‐based authentication methods, such as client and code‐signing, employ cryptographic frameworks like elliptical curve cryptography (ECC) and physical unclonable functions (PUF). However, these methods face challenges, including block sequence issues and susceptibility to side‐channel attacks. To address these issues, we propose a framework for mutual authentication using private Ethereum. This framework integrates private Ethereum and cryptographic techniques for encrypting and decrypting data using mathematical algorithms to overcome block sequence issues and side‐channel attacks. Similarly, fog nodes are utilised to enhance local computing, storage, and networking capabilities for sensors. The framework is evaluated using metrics such as communication costs, execution costs, and computation costs based on Ethereum gas consumption. The performance of the LightAuth framework is compared with that of the Smart Contracts Against Counterfeit IoMT (SCACIoMT) framework, designed for Internet of Medical Things (IoMT) devices. The effectiveness of LightAuth is verified through formal security analysis using BAN logic.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it