  An adversarial deep learning approach is presented to launch over-the-air
spectrum poisoning attacks. A transmitter applies deep learning on its spectrum
sensing results to predict idle time slots for data transmission. In the
meantime, an adversary learns the transmitter's behavior (exploratory attack)
by building another deep neural network to predict when transmissions will
succeed. The adversary falsifies (poisons) the transmitter's spectrum sensing
data over the air by transmitting during the short spectrum sensing period of
the transmitter. Depending on whether the transmitter uses the sensing results
as test data to make transmit decisions or as training data to retrain its deep
neural network, either it is fooled into making incorrect decisions (evasion
attack), or the transmitter's algorithm is retrained incorrectly for future
decisions (causative attack). Both attacks are energy efficient and hard to
detect (stealth) compared to jamming the long data transmission period, and
substantially reduce the throughput. A dynamic defense is designed for the
transmitter that deliberately makes a small number of incorrect transmissions
(selected by the confidence score on channel classification) to manipulate the
adversary's training data. This defense effectively fools the adversary (if
any) and helps the transmitter sustain its throughput with or without an
adversary present.
