LoRa Device Fingerprinting in the Wild: Disclosing RF Data-Driven Fingerprint Sensitivity to Deployment Variability
LoRa Device Fingerprinting in the Wild: Disclosing RF Data-Driven Fingerprint Sensitivity to Deployment Variability
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DOI：
10.1109/access.2021.3121606
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发表时间：
2021-01-01
期刊：
影响因子：
3.9
通讯作者：
Hamdaoui, Bechir
中科院分区：
文献类型：
--
作者：
Elmaghbub, Abdurrahman;Hamdaoui, Bechir
基于深度学习的指纹识别技术最近已成为各种无线应用的潜在推动因素。然而，它们在操作环境中对时间、位置和/或配置变化的适应性无疑仍然是其部署过程中面临的一个主要挑战。在本文中，我们提出了一个实验框架，旨在揭示、理解和克服LoRa设备指纹识别对部署设置变化的敏感性。我们首先介绍了我们的射频指纹数据集，这些数据是从25个不同的LoRa设备收集而来的。该数据集涵盖了一套全面的实验场景，考虑了具有不同网络部署设置的室内和室外环境，例如改变发射器和接收器之间的距离、LoRa协议的配置、进行实验的物理位置以及用于捕获指纹的接收器硬件。然后，我们提出了一种新技术，该技术利用由特定设备硬件损伤导致的带外频谱失真来提供独特的设备特征，我们利用这些特征来提高指纹识别的准确性。最后，我们进行了一项实验研究，该研究揭示了基于深度学习的射频指纹识别对各种部署设置变化的敏感性，同时考虑了学习模型输入的三种数据表示形式：时域IQ、频域FFT和幅度/相位极坐标。我们发现，当在相同的部署设置下进行训练和测试时，学习模型表现相对较好，其中FFT表示性能最佳，其次是IQ表示。然而，当在不同的设置下进行训练和测试时，（i）当信道条件改变时，模型无法保持其高准确性，并且（ii）当LoRa配置和/或USRP接收器硬件改变时，模型完全失去对设备进行分类的能力。此外，我们有趣地观察到，当在不同的部署设置下进行训练和测试时，无论设置变化的类型如何，FFT表示的性能都极差。
Deep learning-based fingerprinting techniques have recently emerged as potential enablers of various wireless applications. However, their resiliency to time, location, and/or configuration changes in the operating environment undoubtedly remains one major challenge that lies ahead in their deployment pathway. In this paper, we present an experimental framework that aims to disclose, understand and overcome the sensitivity of LoRa device fingerprinting to variations in deployment settings. We first began by presenting our RF fingerprinting datasets, collected from 25 different LoRa devices. The datasets cover a comprehensive set of experimental scenarios, considering both indoor and outdoor environments with varying network deployment settings, such as varying the distance between the transmitters and the receiver, the configuration of the LoRa protocol, the physical location of the conducted experiment, and the receiver hardware used for capturing the fingerprints. We then proposed a new technique that leverages out-of-band spectrum distortions, that are caused by device-specific hardware impairments, to provide unique device signatures that we exploit to improve fingerprinting accuracy. Finally, we conducted an experimental study that discloses the sensitivity of deep learning-based RF fingerprinting to changes in various deployment settings while considering three data representations of the learning model input: time-domain IQ, frequency-domain FFT, and Amplitude/Phase polar-coordinate. We found that the learning models perform relatively well when trained and tested under the same deployment settings, with FFT representation yielding the best performance followed by IQ representation. However, when trained and tested under different settings, the models (i) fail to maintain their high accuracy when the channel conditions change, and (ii) completely lose their ability to classify devices when the LoRa configuration and/or the USRP receiver hardware change. In addition, we interestingly observed that FFT representation performs exceptionally poorly when training and testing are done under different deployment settings, regardless of the type of the setting change.