No Radio Left Behind: Radio Fingerprinting Through Deep Learning of Physical-Layer Hardware Impairments
No Radio Left Behind: Radio Fingerprinting Through Deep Learning of Physical-Layer Hardware Impairments
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DOI：
10.1109/tccn.2019.2949308
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发表时间：
2020-03-01
影响因子：
8.6
通讯作者：
Chowdhury, Kaushik
中科院分区：
文献类型：
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作者：
Sankhe, Kunal;Belgiovine, Mauro;Chowdhury, Kaushik
由于物联网的空前规模，设计可扩展、准确、节能和防篡改的认证机制变得比以往任何时候都更加重要。为此，在本文中，我们提出了ORACLE，一个基于卷积神经网络（cnn）的新系统，通过深度学习无线电电路对物理层I/Q样本施加的细粒度硬件损伤，从大量设备中“指纹”（即识别）一个唯一的无线电。首先，我们展示了CNN框架是如何学习硬件特定缺陷的。然后，我们广泛评估了ORACLE在“野外”收集的几个同类首创的大规模WiFi传输数据集以及名义上相同（即基带信号相等）WiFi设备的数据集上的性能，在许多情况下达到80-90%的精度，其中由于信道诱导效应而产生的误差间隙。最后，我们通过实验测试平台展示了如何通过故意插入和学习发射机侧可控损伤的影响，从而达到99%以上的精度，从而完全消除无线信道的影响。此外，为了将这种方法扩展到对潜在的数千个无线电进行分类，我们提出了一种抗欺骗攻击的损伤跳频（IHOP）技术。
Due to the unprecedented scale of the Internet of Things, designing scalable, accurate, energy-efficient and tamper-proof authentication mechanisms has now become more important than ever. To this end, in this paper we present ORACLE, a novel system based on convolutional neural networks (CNNs) to "fingerprint" (i.e., identify) a unique radio from a large pool of devices by deep-learning the fine-grained hardware impairments imposed by radio circuitry on physical-layer I/Q samples. First, we show how hardware-specific imperfections are learned by the CNN framework. Then, we extensively evaluate the performance of ORACLE on several first-of-its-kind large-scale datasets of WiFi-transmissions collected "in the wild", as well as a dataset of nominally-identical (i.e., equal baseband signals) WiFi devices, reaching 80-90% accuracy is many cases with the error gap arising due to channel-induced effects. Finally, we show through an experimental testbed, how this accuracy can reach over 99% by intentionally inserting and learning the effect of controlled impairments at the transmitter side, to completely remove the impact of the wireless channel. Furthermore, to scale this approach for classifying potential thousands of radios, we propose an impairment hopping spread spectrum (IHOP) technique that is resilient to spoofing attacks.