Deep Learning-based RF fingerprinting approaches struggle to perform well in cross-domain scenarios, particularly during hardware warm-up. This often-overlooked vulnerability has been jeopardizing their reliability and their adoption in practical settings. To address this critical gap, in this work, we first dive deep into the anatomy of RF fingerprints, revealing insights into the temporal fingerprinting variations during and post hardware stabilization. Introducing HEEDFUL, a novel framework harnessing sequential transfer learning and targeted impairment estimation, we then address these challenges with remarkable consistency, eliminating blind spots even during challenging warm-up phases. Our evaluation showcases HEEDFUL's efficacy, achieving remarkable classification accuracies of up to 96% during the initial device operation intervals-far surpassing traditional models. Furthermore, cross-day and cross-protocol assessments confirm HEEDFUL's superiority, achieving and maintaining high accuracy during both the stable and initial warm-up phases when tested on WiFi signals. Additionally, we release WiFi type B and N RF fingerprint datasets that, for the first time, incorporate both the time-domain representation and real hardware impairments of the frames. This underscores the importance of leveraging hardware impairment data, enabling a deeper understanding of fingerprints and facilitating the development of more robust RF fingerprinting solutions.

翻译：基于深度学习的射频指纹识别方法在跨域场景，尤其是硬件预热期间，往往表现不佳。这一常被忽视的脆弱性已危及其可靠性及在实际环境中的部署应用。为填补这一关键空白，本研究首先深入剖析射频指纹的构成，揭示了硬件稳定期间及稳定后指纹随时间变化的规律。随后，我们提出HEEDFUL这一新颖框架，它利用序列迁移学习及针对性损伤估计，以出色的稳定性应对上述挑战，即便在极具挑战性的预热阶段也能消除识别盲区。评估结果表明HEEDFUL效果显著，在设备初始运行阶段实现了高达96%的分类准确率，远超传统模型。此外，跨日与跨协议评估进一步证实了HEEDFUL的优越性，在WiFi信号测试中，无论稳定阶段还是初始预热阶段，均能实现并保持高准确率。同时，我们发布了WiFi B类与N类射频指纹数据集，该数据集首次同时包含了帧的时域表征及真实的硬件损伤信息。这凸显了利用硬件损伤数据的重要性，有助于更深入地理解射频指纹，并推动开发更鲁棒的射频指纹识别解决方案。