Deep-learning-based device fingerprinting has recently been recognized as a key enabler for automated network access authentication. Its robustness to impersonation attacks due to the inherent difficulty of replicating physical features is what distinguishes it from conventional cryptographic solutions. Although device fingerprinting has shown promising performance, its sensitivity to changes in the network operating environment still poses a major limitation. This article presents an experimental framework that aims to study and overcome the sensitivity of LoRa-enabled device fingerprinting to such changes. We first begin by describing RF datasets we collected using our LoRa-enabled wireless device testbed. We then propose a new fingerprinting technique that exploits out-of-band distortion information caused by hardware impairments to increase the fingerprinting accuracy. Finally, we experimentally study and analyze the sensitivity of LoRa RF finger-printing to various network setting changes. Our results show that fingerprinting does relatively well when the learning models are trained and tested under the same settings. However, when trained and tested under different settings, these models exhibit moderate sensitivity to channel condition changes and severe sensitivity to protocol configuration and receiver hardware changes when IQ data is used as input. However, when FFT data is used as input, they perform poorly under any change.

中文翻译：

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基于深度学习的设备指纹识别可提高 LoRa-IoT 安全性：对网络部署变化的敏感性


基于深度学习的设备指纹识别最近被认为是自动化网络访问身份验证的关键推动者。由于复制物理特征的固有困难，它对假冒攻击的鲁棒性使其与传统加密解决方案的区别。尽管设备指纹识别已显示出良好的性能，但其对网络操作环境变化的敏感性仍然构成了主要限制。本文提出了一个实验框架，旨在研究和克服支持 LoRa 的设备指纹识别对此类变化的敏感性。我们首先描述使用支持 LoRa 的无线设备测试台收集的 RF 数据集。然后，我们提出了一种新的指纹识别技术，该技术利用硬件损伤引起的带外失真信息来提高指纹识别的准确性。最后，我们通过实验研究和分析LoRa射频指纹对各种网络设置变化的敏感性。我们的结果表明，当在相同设置下训练和测试学习模型时，指纹识别效果相对较好。然而，当在不同设置下进行训练和测试时，这些模型对信道条件变化表现出中等敏感性，而当使用 IQ 数据作为输入时，对协议配置和接收器硬件变化表现出严重敏感性。然而，当使用 FFT 数据作为输入时，它们在任何变化下都表现不佳。