通过主动对抗扰动擦除射频指纹
Erasing Radio Frequency Fingerprints via Active Adversarial Perturbation
AI总结：
本文针对射频指纹可能泄露用户隐私的问题，提出一种基于主动对抗扰动的擦除方法，通过扰动导频信号隐藏硬件特征并误导识别模型，同时不影响通信功能，并在LTE系统中验证了其有效性。
摘要：
射频（RF）指纹识别是指根据无线设备模拟电路或硬件缺陷的独特性来识别该设备。然而，与可以修改的MAC地址不同，这种硬件特征对于发射到空中的信号是不可避免的，这可能暴露设备的位置信息，例如，嗅探器可以在接收到附近设备的信号时使用预训练模型来识别该设备。这种指纹可能泄露关键的隐私信息，例如关联的上层应用或最终用户。在本文中，我们提出为无线设备擦除这种RF特征，通过从信号角度进行主动扰动来防止指纹识别。具体来说，我们考虑一种常见的RF指纹识别场景，其中机器学习模型通过导频信号数据进行训练以用于识别。我们设计了一种新颖的对抗攻击解决方案来生成适当的扰动，使得被扰动的导频信号能够隐藏硬件特征并使模型错误分类。我们从理论上证明，在可容忍的扰动阈值内，该扰动不会影响通信功能。我们还在实际的LTE系统中实现了导频信号指纹识别和所提出的扰动过程。大量实验结果表明，RF指纹可以被有效擦除，从而保护用户隐私。
英文摘要：
Radio Frequency (RF) fingerprinting is to identify a wireless device from its uniqueness of the analog circuitry or hardware imperfections. However, unlike the MAC address which can be modified, such hardware feature is inevitable for the signal emitted to air, which can possibly reveal device whereabouts, e.g., a sniffer can use a pre-trained model to identify a nearby device when receiving its signal. Such fingerprint may expose critical private information, e.g., the associated upper-layer applications or the end-user. In this paper, we propose to erase such RF feature for wireless devices, which can prevent fingerprinting by actively perturbation from the signal perspective. Specifically, we consider a common RF fingerprinting scenario, where machine learning models are trained from pilot signal data for identification. A novel adversarial attack solution is designed to generate proper perturbations, whereby the perturbed pilot signal can hide the hardware feature and misclassify the model. We theoretically show that the perturbation would not affect the communication function within a tolerable perturbation threshold. We also implement the pilot signal fingerprinting and the proposed perturbation process in a practical LTE system. Extensive experiment results demonstrate that the RF fingerprints can be effectively erased to protect the user privacy.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
