A Lightweight DNA Inspired Logistic Leo Based Attribute Encryption Scheme for Mutual Authentication in Smart IoT Medical System
Bibliographic record
Abstract
Securing the Internet of Things (IoT) devices remain a real challenge as it is susceptible to diverse security intimidations owing to its heterogeneous nature and infrastructure-less deployment.Therefore, ensuring the authenticity, honesty, and secrecy of sensitive data in the implemented region necessitates the establishment of a mutual authentication mechanism among linking components.Many approaches are proposed in the scientific literature to tackle threats to security in IoT smart healthcare environs.However, deploying existing methodologies in the IoT-based healthcare system requires high computation costs and less secure communication.It is therefore to develop an attribute encryption scheme that can safeguard the IoT devices against attacks in medical environments.This paper recommends a novel lightweight and secured protocol relying on the enhanced attribute-based encryption scheme operates based on the principle of DNA-based Chaotic Leo Attribute Encryption (DNA-CLAE) technique using the suggested architecture, authorized devices may unicast dynamic key authentication and change their keys for each transmission cycle, securely transferring private healthcare information from the source to the destination.Additionally, utilizing widely accepted conventional pairing-based cryptography libraries (PBC), the suggested architecture is implemented on embedded Internet of Things gadgets based on Raspberry Pi and ESP8266, and it is contrasted with other contemporary cutting-edge security proprieties.A thorough and formal verification of the suggested approach is conducted using the Automated Validation of Internet Security Protocol Application (AVISPA) to assess as well as analyze the security strength of the framework.Based on the findings, the suggested strategy has demonstrated strong protective qualities towards both proactive as well as passive threats.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".