Radio frequency fingerprint identification (RFFI) is an emerging technique for the lightweight authentication of wireless Internet of things (IoT) devices. RFFI exploits deep learning models to extract hardware impairments to uniquely identify wireless devices. Recent studies show deep learning-based RFFI is vulnerable to adversarial attacks. However, effective adversarial attacks against different types of RFFI classifiers have not yet been explored. In this paper, we carried out a comprehensive investigations into different adversarial attack methods on RFFI systems using various deep learning models. Three specific algorithms, fast gradient sign method (FGSM), projected gradient descent (PGD), and universal adversarial perturbation (UAP), were analyzed. The attacks were launched to LoRa-RFFI and the experimental results showed the generated perturbations were effective against convolutional neural networks (CNNs), long short-term memory (LSTM) networks, and gated recurrent units (GRU). We further used UAP to launch practical attacks. Special factors were considered for the wireless context, including implementing real-time attacks, the effectiveness of the attacks over a period of time, etc. Our experimental evaluation demonstrated that UAP can successfully launch adversarial attacks against the RFFI, achieving a success rate of 81.7% when the adversary almost has no prior knowledge of the victim RFFI systems.

翻译：射频指纹识别（RFFI）是一种用于无线物联网（IoT）设备轻量级认证的新兴技术。RFFI利用深度学习模型提取硬件损伤，以唯一识别无线设备。近期研究表明，基于深度学习的RFFI易受对抗性攻击。然而，针对不同类型RFFI分类器的有效对抗性攻击尚未得到充分探索。本文采用多种深度学习模型，对RFFI系统的不同对抗性攻击方法进行了全面研究。分析了三种具体算法：快速梯度符号法（FGSM）、投影梯度下降法（PGD）和通用对抗扰动（UAP）。攻击针对LoRa-RFFI系统实施，实验结果表明生成的扰动对卷积神经网络（CNN）、长短期记忆（LSTM）网络和门控循环单元（GRU）均具有效性。我们进一步利用UAP发起实际攻击，并考虑了无线环境中的特殊因素，包括实施实时攻击、攻击在一段时间内的有效性等。实验评估表明，UAP能成功对RFFI系统发起对抗性攻击，在攻击者几乎不了解目标RFFI系统先验知识的情况下，攻击成功率可达81.7%。