Recent device fingerprinting approaches rely on deep learning to extract device-specific features solely from raw RF signals to identify, classify and authenticate wireless devices. One widely known issue lies in the inability of these approaches to maintain good performances when the training data and testing data are collected under varying deployment domains. For example, when the learning model is trained on data collected from one receiver but tested on data collected from a different receiver, the performance degrades substantially compared to when both training and testing data are collected using the same receiver. The same also happens when considering other varying domains, like channel condition and protocol configuration. In this paper, we begin by explaining, through testbed experiments, the challenges these fingerprinting techniques face when it comes to domain portability. We will then present some ideas on how to go about addressing these challenges so as to make deep learning-based device fingerprinting more resilient to domain variability.

通过测试实验，本文指出基于深度学习从RF信号中提取特征进行设备指纹识别存在的域可移植性问题，并提出解决方案以提高其抗域可变性能力。

揭示深度学习无线设备指纹的可移植性局限性