RF指纹识别需要关注：现实世界WiFi和蓝牙的多任务方法
RF Fingerprinting Needs Attention: Multi-task Approach for Real-World WiFi and Bluetooth
摘要：
本工作提出了一种新颖的跨域注意力多任务架构- xDom -用于鲁棒的现实世界无线射频（RF）指纹识别。据我们所知，这是第一次这样全面的注意力机制被应用于解决RF指纹问题。在本文中，我们在室内实验测试平台中，在丰富的多路径和不可避免的干扰环境中，采用真实世界的物联网WiFi和蓝牙（BT）发射（而不是合成波形生成）。我们通过包括在几个月的跨度内收集的波形来显示捕获的时间范围的影响，并展示相同的时间范围和多个时间范围的指纹识别评估。采用多任务架构的有效性也通过进行单任务和多任务模型分析实验证明。最后，我们证明了显着的性能增益与建议的xDom架构的基准对一个著名的国家的最先进的模型指纹。具体来说，我们报告了单任务WiFi和BT指纹识别下的性能分别提高了59.3%和4.91倍，多任务设置下的指纹识别准确性提高了50.5%。
英文摘要：
A novel cross-domain attentional multi-task architecture - xDom - for robust real-world wireless radio frequency (RF) fingerprinting is presented in this work. To the best of our knowledge, this is the first time such comprehensive attention mechanism is applied to solve RF fingerprinting problem. In this paper, we resort to real-world IoT WiFi and Bluetooth (BT) emissions (instead of synthetic waveform generation) in a rich multipath and unavoidable interference environment in an indoor experimental testbed. We show the impact of the time-frame of capture by including waveforms collected over a span of months and demonstrate the same time-frame and multiple time-frame fingerprinting evaluations. The effectiveness of resorting to a multi-task architecture is also experimentally proven by conducting single-task and multi-task model analyses. Finally, we demonstrate the significant gain in performance achieved with the proposed xDom architecture by benchmarking against a well-known state-of-the-art model for fingerprinting. Specifically, we report performance improvements by up to 59.3% and 4.91x under single-task WiFi and BT fingerprinting respectively, and up to 50.5% increase in fingerprinting accuracy under the multi-task setting.
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