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Using machine learning for dynamic authentication in telehealth: a tutorial

From National Research Council Canada

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DOIResolve DOI: https://doi.org/10.3390/s22197655
AuthorSearch for: ORCID identifier: https://orcid.org/0000-0002-2082-2840; Search for: ; Search for: 1ORCID identifier: https://orcid.org/0000-0002-4045-8687
Affiliation
  1. National Research Council of Canada. Digital Technologies
FormatText, Article
Subjecttelehealth; IoT security; dynamic authentication; continuous authentication; machine learning; deep learning
Abstract
Telehealth systems have evolved into more prevalent services that can serve people in remote locations and at their homes via smart devices and 5G systems. Protecting the privacy and security of users is crucial in such online systems. Although there are many protocols to provide security through strong authentication systems, sophisticated IoT attacks are becoming more prevalent. Using machine learning to handle biometric information or physical layer features is key to addressing authentication problems for human and IoT devices, respectively. This tutorial discusses machine learning applications to propose robust authentication protocols. Since machine learning methods are trained based on hidden concepts in biometric and physical layer data, these dynamic authentication models can be more reliable than traditional methods. The main advantage of these methods is that the behavioral traits of humans and devices are tough to counterfeit. Furthermore, machine learning facilitates continuous and context-aware authentication.
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PublisherMDPI AG
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  • Sensors 22, no. 19, 7655 (9 October 2022).
LanguageEnglish
Peer reviewedYes
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Record identifier27d306cb-449a-4392-a2a0-ad2ef79118e9
Record created2023-01-20
Record modified2023-01-20
Date modified: