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 performances, its sensitivity to changes in the network operating environment still poses a major limitation. This paper 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 fingerprinting 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, with FFT data is used as input, they perform poorly under any change.
翻译:最近,人们认识到,基于深层学习的装置指纹是自动网络访问认证的关键促进器。由于复制物理特征的固有困难,它对于假冒攻击的坚固性是其与常规加密解决方案的区别。虽然设备指纹显示有良好的性能,但对网络操作环境变化的敏感度仍是一个重大限制。本文展示了一个实验框架,目的是研究和克服LoRa驱动的装置指纹对此类变化的敏感性。我们首先从描述我们用我们的LoRa驱动的无线设备测试床收集的RF数据集开始,然后我们提出一种新的指纹技术,利用硬件缺陷造成的带外扭曲信息来提高指纹准确性。最后,我们实验性地研究和分析LoRa RF指纹对网络设置变化的敏感性。我们的结果显示,当学习模型在相同的环境下接受培训和测试时,指纹效果相对较好。然而,当在不同环境下培训和测试时,这些模型显示对频道状况变化的敏感度和在使用IQ数据时对协议配置和接收器硬件变化的高度敏感度。但是,在使用FFT数据时,在任何输入时,其使用不甚差。