Deep learning-based radio frequency fingerprinting (RFFP) has become an enabling physical-layer security technology, allowing device identification and authentication through received RF signals. This technology, however, faces significant challenges when it comes to adapting to domain variations, such as time, location, environment, receiver and channel. For Bluetooth Low Energy (BLE) devices, addressing these challenges is particularly crucial due to the BLE protocol's frequency-hopping nature. In this work, and for the first time, we investigated the frequency hopping effect on RFFP of BLE devices, and proposed a novel, low-cost, domain-adaptive feature extraction method. Our approach improves the classification accuracy by up to 58\% across environments and up to 80\% across receivers compared to existing benchmarks.
翻译：基于深度学习的射频指纹识别已成为一种关键的物理层安全技术，可通过接收到的射频信号实现设备识别与认证。然而，该技术在适应域变化（如时间、位置、环境、接收机和信道）方面面临重大挑战。对于蓝牙低功耗设备，由于BLE协议的跳频特性，应对这些挑战尤为关键。在本研究中，我们首次探究了跳频对BLE设备射频指纹识别的影响，并提出了一种新颖、低成本的域自适应特征提取方法。与现有基准相比，我们的方法在不同环境下的分类准确率提升高达58%，在不同接收机间的准确率提升高达80%。
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