A comprehensive review of IoT device fingerprinting: Insights into techniques, trends, challenges, and future directions

The Internet of Things (IoT) connects billions of devices, ranging from household appliances to industrial systems, enabling intelligent automation, real-time monitoring, and seamless communication. However, the rapid expansion in the IoT ecosystem introduces significant security and management challenges, particularly in device identification and authentication. IoT device fingerprinting has emerged as a critical research area for enhancing security and management in interconnected ecosystems.

The article at hand provides a comprehensive review and analysis of existing IoT device fingerprinting methods from 2017 to 2025. It categorizes these methods based on their underlying approaches across the Physical, Network, and Application communication layers. Each study is critically examined, with a focus on its characteristics, strengths, and limitations. The article also reviews publicly available datasets and explores trends in feature selection, including the use of statistical, radio frequency, and network packet features. Moreover, it also examines the adoption of machine learning and deep learning models in this context. Finally, the article addresses existing challenges, use cases, and outlines future research directions to support the development of more effective and scalable solutions in this domain.