Cybersecurity Architecture for Medical Live Monitoring Systems Using HTTP Protocols and SHA-256 Encryption

Clicks: 1
ID: 312596
2026
Free PDF
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This article has not been analysed, so there is no overall score — reader engagement is measured and shown alongside.
AI Quality Assessment
Not analyzed
Readership in this journal

Ranked #118 of 705 articles by views in Journal of Computing & Biomedical Informatics

Most read Least read

Bar heights use a square-root scale. Only the 120 most-read articles are drawn; the journal has 705 in total.

Mint this article as an NFT
Not yet minted

Create a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.

5 SUSD one-off · no wallet required
Sign in to mint
Abstract
Live health monitoring systems are revolutionizing patient care, yet the security and integrity of sensitive medical data remain a critical challenge. To address the vulnerabilities of centralized databases and basic authentication in traditional systems, we propose a secure framework that leverages a decentralized architecture. This proposed system uses standard HTTPS protocols for communication, with each client request authenticated using JSON Web Tokens (JWT) signed with HMAC-SHA256. Patient records are encrypted using AES-256-GCM before storage, with cryptographic hashes of these records written to a private Hyperledger Fabric blockchain for tamper-proof auditability. A smart contract enforces decentralized, role-based access control to ensure only authorized personnel can view sensitive information. A predictive analytics module processes secured vital data to forecast potential health deterioration, triggering automated alerts to the responsible physician. In a simulation handling 1,000 patient records over 30 days, the system demonstrated 99.98% data integrity, processed an average of 4,820 authenticated requests per minute with a mean latency of 185ms, and achieved 87.3% accuracy in predicting critical health events with a 30-minute lead time, confirming its efficacy as a robust and secure solution for remote patient monitoring.
Reference Key
imported_1777055147_69ebb5ab738f2 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Justindhas Y
Journal Journal of Computing & Biomedical Informatics
Year 2026
DOI
DOI not found
URL
https://jcbi.org/index.php/Main/article/view/1252
Keywords Keywords not found

Citations

No citations found. To add a citation, contact the admin at info@scimatic.org

Comments

No comments yet. Be the first to comment on this article.