标题： 基于对抗驱动的深度学习在射频指纹识别中的实验研究 显示英文标题

标题： An Adversarial-Driven Experimental Study on Deep Learning for RF Fingerprinting

摘要： 射频（RF）指纹技术，通过提取无线设备的独特硬件缺陷，已成为零信任架构和5G网络之外的有前途的物理层设备识别机制。 特别是，深度学习（DL）方法在这一领域表现出最先进的性能。 然而，现有的方法主要集中在增强系统对无线环境中的时间性和空间性变化的鲁棒性，而这些基于DL的方法的安全漏洞常常被忽视。 在本工作中，我们通过对抗驱动的实验分析系统地研究了基于DL的RF指纹系统的安全风险。 我们观察到，在领域转移下，DL模型存在一致的误分类行为，其中设备经常被错误分类为另一个特定设备。 我们的分析基于广泛的现实实验表明，这种行为可以被利用作为有效的后门，使外部攻击者能够入侵系统。 此外，我们表明，在原始接收信号上训练DL模型会导致模型将RF指纹与环境和信号模式特征纠缠在一起，从而产生额外的攻击向量，这些向量无法仅通过置信度阈值等后期处理安全方法来缓解。 显示英文摘要

摘要： Radio frequency (RF) fingerprinting, which extracts unique hardware imperfections of radio devices, has emerged as a promising physical-layer device identification mechanism in zero trust architectures and beyond 5G networks. In particular, deep learning (DL) methods have demonstrated state-of-the-art performance in this domain. However, existing approaches have primarily focused on enhancing system robustness against temporal and spatial variations in wireless environments, while the security vulnerabilities of these DL-based approaches have often been overlooked. In this work, we systematically investigate the security risks of DL-based RF fingerprinting systems through an adversarial-driven experimental analysis. We observe a consistent misclassification behavior for DL models under domain shifts, where a device is frequently misclassified as another specific one. Our analysis based on extensive real-world experiments demonstrates that this behavior can be exploited as an effective backdoor to enable external attackers to intrude into the system. Furthermore, we show that training DL models on raw received signals causes the models to entangle RF fingerprints with environmental and signal-pattern features, creating additional attack vectors that cannot be mitigated solely through post-processing security methods such as confidence thresholds.