Using machine learning for dynamic authentication in telehealth: A tutorial

Date

2022

Authors

Hazratifard, Mehdi
Gebali, Fayez
Mamun, Mohammad

Journal Title

Journal ISSN

Volume Title

Publisher

Sensors

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.

Description

Keywords

telehealth, IoT security, dynamic authentication, continuous authentication, machine learning, deep learning

Citation

Hazratifard, M., Gebali, F., & Mamun, M. (2022). “Using machine learning for dynamic authentication in telehealth: A tutorial.” Sensors, 22(19), 7655. https://doi.org/10.3390/s22197655