Hacking the Waveform：Generalized Wireless Adversarial深度学习
Hacking the Waveform: Generalized Wireless Adversarial Deep Learning
摘要：
本文通过提出针对无线深度学习系统的对抗性学习攻击的第一次全面分析和实验评估，推进了最新技术水平。我们假设了一系列的对抗攻击，并制定了一个广义无线对抗机器学习问题（GWAP），我们分析了无线信道和对抗波形对攻击的有效性的综合影响。我们提出了一种新的神经网络架构，称为FIRNet，它可以被训练成只根据其输出来“破解”分类器。我们广泛评估了（i）1，000个设备的无线电指纹数据集和（ii）24类调制数据集的性能。几种信道条件下获得的结果表明，我们的算法可以降低分类器的准确性高达3倍。我们还在无线电测试平台上对FIRNet进行了实验评估，并表明我们的数据驱动黑盒方法可以混淆高达97%的分类器，同时将波形失真保持在最低限度。
英文摘要：
This paper advances the state of the art by proposing the first comprehensive analysis and experimental evaluation of adversarial learning attacks to wireless deep learning systems. We postulate a series of adversarial attacks, and formulate a Generalized Wireless Adversarial Machine Learning Problem (GWAP) where we analyze the combined effect of the wireless channel and the adversarial waveform on the efficacy of the attacks. We propose a new neural network architecture called FIRNet, which can be trained to "hack" a classifier based only on its output. We extensively evaluate the performance on (i) a 1,000-device radio fingerprinting dataset, and (ii) a 24-class modulation dataset. Results obtained with several channel conditions show that our algorithms can decrease the classifier accuracy up to 3x. We also experimentally evaluate FIRNet on a radio testbed, and show that our data-driven blackbox approach can confuse the classifier up to 97% while keeping the waveform distortion to a minimum.
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