The spoofing attack is critical to bypass physical-layer signal authentication. This paper presents a deep learning-based spoofing attack to generate synthetic wireless signals that cannot be statistically distinguished from intended transmissions. The adversary is modeled as a pair of a transmitter and a receiver that build the generator and discriminator of the generative adversarial network, respectively, by playing a minimax game over the air. The adversary transmitter trains a deep neural network to generate the best spoofing signals and fool the best defense trained as another deep neural network at the adversary receiver. Each node (defender or adversary) may have multiple transmitter or receiver antennas. Signals are spoofed by jointly capturing waveform, channel, and radio hardware effects that are inherent to wireless signals under attack. Compared with spoofing attacks using random or replayed signals, the proposed attack increases the probability of misclassifying spoofing signals as intended signals for different network topology and mobility patterns. The adversary transmitter can increase the spoofing attack success by using multiple antennas, while the attack success decreases when the defender receiver uses multiple antennas. For practical deployment, the attack implementation on embedded platforms demonstrates the low latency of generating or classifying spoofing signals.

中文翻译：

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空中生成对抗网络：无线信号欺骗的深度对抗学习

欺骗攻击对于绕过物理层信号身份验证至关重要。本文提出了一种基于深度学习的欺骗攻击，以生成无法从统计上区分与预期传输的合成无线信号。对手被建模为一对发射器和接收器，它们分别通过空中玩极小极大游戏来构建生成对抗网络的生成器和鉴别器。敌手发射器训练一个深度神经网络来生成最好的欺骗信号，并欺骗在敌手接收器上训练为另一个深度神经网络的最佳防御。每个节点（防御者或对手）可能有多个发射器或接收器天线。通过联合捕获波形、通道、以及受到攻击的无线信号所固有的无线电硬件效应。与使用随机或重放信号的欺骗攻击相比，所提出的攻击增加了将欺骗信号错误分类为不同网络拓扑和移动模式的预期信号的可能性。敌方发射机使用多根天线可以提高欺骗攻击的成功率，而防御方接收机使用多根天线会降低攻击成功率。对于实际部署，嵌入式平台上的攻击实现证明了生成或分类欺骗信号的低延迟。敌方发射方使用多根天线可以提高欺骗攻击的成功率，而防御方接收方使用多根天线则攻击成功率降低。对于实际部署，嵌入式平台上的攻击实现证明了生成或分类欺骗信号的低延迟。敌方发射机使用多根天线可以提高欺骗攻击的成功率，而防御方接收机使用多根天线会降低攻击成功率。对于实际部署，嵌入式平台上的攻击实现证明了生成或分类欺骗信号的低延迟。