Robust authentication mechanisms integrating biometrics and AI for securing remote access to cloud-based healthcare services
Department of Information Systems Lamar University, Beaumont Texas.
Research Article
World Journal of Advanced Research and Reviews, 2024, 23(03), 034–044
Article DOI: 10.30574/wjarr.2024.23.3.2642
Publication history:
Received on 22 July 2024; revised on 29 August 2024; accepted on 31 August 2024
Abstract:
Strong authentication is needed to protect remote patient health data in cloud-based healthcare services. Traditional verification techniques like passwords or tokens are hard to remember, share, or steal. This project aims to secure online cloud healthcare platform access using biometrics and AI. Biometrics is the finest cloud healthcare user authentication option, while others have issues with utility, accuracy, and fraud. Research reveals that biometric approaches may authenticate persons well, but cloud storage can compromise privacy. Machine learning aids biometric recognition, but current systems don't balance accuracy, scalability, and security. This article shows a multi-factor login solution using device biometrics and cloud AI. Remote log-ins are verified by fingerprints and facial recognition on mobile apps. Anomaly detection finds illogical attempts to get access, and machine learning models use biometric templates to authenticate persons. This biometric-AI technique seeks a balance between strong authentication, privacy, and usability. System design includes mobile client biometric sensors, template extractor module, AI authentication server, and cloud healthcare databases. Faces and fingerprints are processed locally before sending a safe encrypted template. Deep learning recognition models verify templates against server enrollment records. An isolation forest algorithm detects unusual login trends for security. The authentication method was tested on 50 people for five weeks. Fingerprint and face recognition outperformed passwords with FARs of 0.0008% and FRRs of 0.2%. The AI detector thwarted 99.9% of simulated cyberattacks and scored 4.5/5 for usability. A comparison study found that the multi-factor system was more accurate, trustworthy, and scalable than single biometric or password schemes. The study developed and evaluated a privacy-preserving biometric-AI identification system for remote cloud healthcare access. The multi-modal solution was easy to use and secure against multiple attack scenarios. With continued scalability and inclusiveness advancements, it could be used in many therapeutic settings worldwide.
Keywords:
Biometric authentication; Artificial intelligence; Cloud-based healthcare services; Remote access; Security; Privacy
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Copyright © 2024 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0