Designing reliable authentication mechanisms for IoT devices is increasingly necessary to protect citizens’ private information and data. One of the most significant issues in today’s digital age is authentication. As IoT device technology advances and data grows rapidly, machine learning techniques present a viable resource to enhance the accuracy and efficiency of the authentication process. The machine learning methods employed for device authentication offer several advantages over traditional approaches, making them essential in both education and industry. Device authentication aims to verify legitimate computing devices and identify impostors based on their behavioural data. This paper explores research that applies artificial intelligence algorithms to enhance device authentication mechanisms. We focus on lightweight, adaptable, and scalable ML-based authentication systems that bridge the gap between theoretical discovery and practical implementation. In addition, we discuss AI authentication models, including deep learning algorithms, convolutional neural networks, and reinforcement learning and present research challenges along with recommendations for future research initiatives to support innovation in this field.
Kelaniki et al. (Thu,) studied this question.