用扩散模型提升低信噪比下的无线设备指纹识别准确率
Noise-Robust Radio Frequency Fingerprint Identification Using Denoise Diffusion Model
- 用扩散模型预测并去除信号噪声，恢复硬件指纹特征
- 在低信噪比下最高提升分类准确率34.9%
- 适合物联网安全、无线设备认证方向的研究者
由于物联网设备计算与能源资源有限，其安全防护面临挑战。射频指纹识别（RFFI）通过硬件缺陷实现无线设备身份认证，具有潜力。但在低信噪比（SNR）条件下，微弱的硬件特征易被噪声淹没，导致性能下降。本文利用扩散模型有效恢复低SNR下的射频指纹。具体而言，训练了强大的噪声预测器，并设计了针对性去噪算法，显著降低接收信号噪声水平，还原设备指纹。以Wi-Fi为案例，搭建包含6个商用无线网卡与USRP N210软件定义无线电平台的测试床，在多种SNR场景下进行实验。结果表明，所提算法可使分类准确率最高提升34.9%。
原文摘要 · Abstract (English)
Securing Internet of Things (IoT) devices presents increasing challenges due to their limited computational and energy resources. Radio Frequency Fingerprint Identification (RFFI) emerges as a promising authentication technique to identify wireless devices through hardware impairments. RFFI performance under low signal-to-noise ratio (SNR) scenarios is significantly degraded because the minute hardware features can be easily swamped in noise. In this paper, we leveraged the diffusion model to effectively restore the RFF under low SNR scenarios. Specifically, we trained a powerful noise predictor and tailored a noise removal algorithm to effectively reduce the noise level in the received signal and restore the device fingerprints. We used Wi-Fi as a case study and created a testbed involving 6 commercial off-the-shelf Wi-Fi dongles and a USRP N210 software-defined radio (SDR) platform. We conducted experimental evaluations on various SNR scenarios. The experimental results show that the proposed algorithm can improve the classification accuracy by up to 34.9%.
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