  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.
