RadioNet: Robust Deep-Learning Based Radio Fingerprinting
RadioNet: Robust Deep-Learning Based Radio Fingerprinting
复制标题
DOI：
10.1109/cns56114.2022.9947255
复制
发表时间：
2022-10
期刊：
影响因子：
--
通讯作者：
Haipeng Li;Kaustubh Gupta;Chenggang Wang;Nirnimesh Ghose;Boyang Wang
中科院分区：
文献类型：
--
作者：
Haipeng Li;Kaustubh Gupta;Chenggang Wang;Nirnimesh Ghose;Boyang Wang
无线电指纹识别通过利用嵌入在射频（RF）信号中的硬件缺陷来识别无线设备。虽然神经网络已被应用于无线电指纹识别，以提高准确性，现有的研究是不健全的，由于两个主要原因。首先，在RF信号的预处理中缺乏信息性参数选择。其次，基于深度学习的无线电指纹识别在跨天场景中的时间变化方面表现不佳。本文从预处理中的参数选择、学习方法和评价指标三个方面增强了基于深度学习的无线电指纹识别的鲁棒性。首先，我们进行了大量的实验，以证明在预处理参数的粗心选择可能会导致过于乐观的结论，无线电指纹的性能。其次，我们利用对抗域自适应来提高跨天场景中无线电指纹的性能。我们的研究结果表明，对抗域自适应可以提高跨天场景中无线电指纹识别的性能，而无需跨天重新收集大规模RF信号。第三，我们引入设备排名作为衡量无线电指纹识别性能的额外指标，与单独使用准确性相比。我们的研究结果表明，追求极高的精度并不总是必要的无线电指纹。当我们使用设备排名进行测量时，合理地大于随机猜测的准确度可能会在一秒钟内导致成功的身份验证。
Radio fingerprinting identifies wireless devices by leveraging hardware imperfections embedded in radio frequency (RF) signals. While neural networks have been applied to radio fingerprinting to improve accuracy, existing studies are not robust due to two major reasons. First, there is a lack of informative parameter selections in pre-processing over RF signals. Second, deep-learning-based radio fingerprinting derives poor performance against temporal variations in the cross-day scenario. In this paper, we enhance the robustness of deep-learning-based radio fingerprinting from three aspects, including parameter selection in pre-processing, learning methods, and evaluation metrics. First, we conduct extensive experiments to demonstrate that careless selections of parameters in pre-processing can lead to over-optimistic conclusions regarding the performance of radio fingerprinting. Second, we leverage adversarial domain adaptation to improve the performance of radio fingerprinting in the cross-day scenario. Our results show that adversarial domain adaptation can improve the performance of radio fingerprinting in the cross-day scenario without the need of recollecting large-scale RF signals across days. Third, we introduce device rank as an additional metric to measure the performance of radio fingerprinting compared to using accuracy alone. Our results show that pursuing extremely high accuracy is not always necessary in radio fingerprinting. An accuracy that is reasonably greater than random guess could lead to successful authentication within a second when we measure with device rank.