Department of Electrical and Computer Engineering, University of Victoria, Victoria, BC V8W 2Y2, Canada.
National Research Council of Canada, Government of Canada, Ottawa, ON K1A 0R6, Canada.
Sensors (Basel). 2022 Oct 9;22(19):7655. doi: 10.3390/s22197655.
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.
远程医疗系统已经发展成为更普及的服务，可以通过智能设备和 5G 系统为远程和居家的人们提供服务。在这样的在线系统中，保护用户的隐私和安全至关重要。虽然有许多协议可以通过强大的认证系统提供安全性，但复杂的物联网攻击变得越来越普遍。使用机器学习来处理生物识别信息或物理层特征是解决人类和物联网设备认证问题的关键。本教程讨论了机器学习应用程序，以提出强大的认证协议。由于机器学习方法是基于生物识别和物理层数据中的隐藏概念进行训练的，因此这些动态认证模型比传统方法更可靠。这些方法的主要优点是，人类和设备的行为特征很难被伪造。此外，机器学习实现了持续和上下文感知的认证。
