RF data-driven device fingerprinting through the use of deep learning has recently surfaced as a possible method for enabling secure device identification and authentication. Traditional approaches are commonly susceptible to the domain adaptation problem where a model trained on data collected under one domain performs badly when tested on data collected under a different domain. Some examples of a domain change include varying the location or environment of the device and varying the time or day of the data collection. In this work, we propose using multifractal analysis and the variance fractal dimension trajectory (VFDT) as a data representation input to the deep neural network to extract device fingerprints that are domain generalizable. We analyze the effectiveness of the proposed VFDT representation in detecting device-specific signatures from hardware-impaired IQ (in-phase and quadrature) signals, and we evaluate its robustness in real-world settings, using an experimental testbed of 30 WiFi-enabled Pycom devices. Our experimental results show that the proposed VFDT representation improves the scalability, robustness and generalizability of the deep learning models significantly compared to when using IQ data samples.

翻译：射频数据驱动的设备指纹识别通过深度学习技术，近期已成为一种实现安全设备识别与认证的潜在方法。传统方法普遍面临域适应问题：在某一域下采集数据训练的模型，在另一域下测试时表现不佳。域变化的实例包括设备位置或环境的改变，以及数据采集时间或日期的差异。本研究提出采用多重分形分析与方差分形维数轨迹（VFDT）作为深度神经网络的输入数据表示，以提取具有域泛化能力的设备指纹。我们分析了所提出的VFDT表示在检测硬件受损IQ（同相与正交）信号中设备特定特征的有效性，并通过包含30个WiFi启用的Pycom设备的实验测试平台，在真实场景中评估其鲁棒性。实验结果表明，相较于直接使用IQ数据样本，所提出的VFDT表示显著提升了深度学习模型的可扩展性、鲁棒性与泛化能力。