Using Machine Learning for Dynamic Authentication in Telehealth: A Tutorial
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 st...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-10-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/22/19/7655 |
| _version_ | 1797476830434820096 |
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| author | Mehdi Hazratifard Fayez Gebali Mohammad Mamun |
| author_facet | Mehdi Hazratifard Fayez Gebali Mohammad Mamun |
| author_sort | Mehdi Hazratifard |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-09T21:09:19Z |
| format | Article |
| id | doaj.art-8b46e4f5018b47358b5559d02c609023 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T21:09:19Z |
| publishDate | 2022-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-8b46e4f5018b47358b5559d02c6090232023-11-23T21:52:40ZengMDPI AGSensors1424-82202022-10-012219765510.3390/s22197655Using Machine Learning for Dynamic Authentication in Telehealth: A TutorialMehdi Hazratifard0Fayez Gebali1Mohammad Mamun2Department of Electrical and Computer Engineering, University of Victoria, Victoria, BC V8W 2Y2, CanadaDepartment of Electrical and Computer Engineering, University of Victoria, Victoria, BC V8W 2Y2, CanadaNational Research Council of Canada, Government of Canada, Ottawa, ON K1A 0R6, CanadaTelehealth 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.https://www.mdpi.com/1424-8220/22/19/7655telehealthIoT securitydynamic authenticationcontinuous authenticationmachine learningdeep learning |
| spellingShingle | Mehdi Hazratifard Fayez Gebali Mohammad Mamun Using Machine Learning for Dynamic Authentication in Telehealth: A Tutorial Sensors telehealth IoT security dynamic authentication continuous authentication machine learning deep learning |
| title | Using Machine Learning for Dynamic Authentication in Telehealth: A Tutorial |
| title_full | Using Machine Learning for Dynamic Authentication in Telehealth: A Tutorial |
| title_fullStr | Using Machine Learning for Dynamic Authentication in Telehealth: A Tutorial |
| title_full_unstemmed | Using Machine Learning for Dynamic Authentication in Telehealth: A Tutorial |
| title_short | Using Machine Learning for Dynamic Authentication in Telehealth: A Tutorial |
| title_sort | using machine learning for dynamic authentication in telehealth a tutorial |
| topic | telehealth IoT security dynamic authentication continuous authentication machine learning deep learning |
| url | https://www.mdpi.com/1424-8220/22/19/7655 |
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