Using Machine Learning for Dynamic Authentication in Telehealth: A Tutorial.

Area of Science:

• Computer Science
• Cybersecurity
• Machine Learning

Background:

• Telehealth systems are increasingly prevalent, offering remote healthcare access via smart devices and 5G.
• Ensuring user privacy and security is paramount in these online systems.
• Sophisticated Internet of Things (IoT) attacks are a growing concern, challenging traditional security protocols.

Purpose of the Study:

• To explore machine learning applications for developing robust authentication protocols in telehealth.
• To address the limitations of traditional authentication methods against advanced cyber threats.
• To highlight the benefits of machine learning in securing human and IoT device access.

Main Methods:

• Utilizing machine learning to analyze biometric information for human authentication.
• Employing machine learning to process physical layer features for IoT device authentication.
• Developing dynamic authentication models trained on hidden patterns in user and device data.

Main Results:

• Machine learning-based authentication models demonstrate increased reliability compared to traditional methods.
• Behavioral traits of humans and devices are identified as difficult-to-counterfeit security features.
• Machine learning facilitates continuous and context-aware authentication, adapting to changing user and device behaviors.

Conclusions:

• Machine learning offers a promising approach to significantly improve telehealth security and user privacy.
• Dynamic, behavior-based authentication powered by machine learning provides a more robust defense against evolving cyber threats.
• The integration of machine learning enables more secure, adaptive, and reliable remote healthcare systems.