RF data-driven device fingerprinting through the use of deep learning has recently surfaced as a potential solution for automated network access authentication. Traditional approaches are commonly susceptible to the domain adaptation problem where a model trained on data from one domain performs badly when tested on data from a different domain. Some examples of a domain change include varying the device location or environment and varying the time or day of 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 signals, and evaluate its robustness in real-world settings, using an experimental testbed of 30 WiFi-enabled Pycom devices under different locations and at different scales. Our results show that the VFDT representation improves the scalability, robustness and generalizability of the deep learning models significantly compared to when using raw IQ data.

翻译：基于射频数据的深度学习设备指纹识别技术近期已成为自动化网络接入认证的一种潜在解决方案。传统方法普遍面临域适应问题，即在一个领域数据上训练的模型在测试不同领域数据时性能显著下降。域变化的典型示例包括设备位置或环境发生改变，以及数据采集时间或日期发生变化。本研究提出采用多重分形分析及方差分形维度轨迹（VFDT）作为深度神经网络的数据表示输入，以提取具有域泛化能力的设备指纹。我们通过搭建包含30个支持WiFi的Pycom设备的实验平台，在不同位置和不同尺度下评估了所提出的VFDT表示在检测硬件受损IQ信号中设备特定特征的有效性，并验证了其在实际环境中的鲁棒性。研究结果表明，相较于直接使用原始IQ数据，VFDT表示能显著提升深度学习模型的可扩展性、鲁棒性和泛化能力。