  Machine learning has been widely applied in wireless communications. However,
the security aspects of machine learning in wireless applications have not been
well understood yet. We consider the case that a cognitive transmitter senses
the spectrum and transmits on idle channels determined by a machine learning
algorithm. We present an adversarial machine learning approach to launch a
spectrum data poisoning attack by inferring the transmitter's behavior and
attempting to falsify the spectrum sensing data over the air. For that purpose,
the adversary transmits for a short period of time when the channel is idle to
manipulate the input for the decision mechanism of the transmitter. The
cognitive engine at the transmitter is a deep neural network model that
predicts idle channels with minimum sensing error for data transmissions. The
transmitter collects spectrum sensing data and uses it as the input to its
machine learning algorithm. In the meantime, the adversary builds a cognitive
engine using another deep neural network model to predict when the transmitter
will have a successful transmission based on its spectrum sensing data. The
adversary then performs the over-the-air spectrum data poisoning attack, which
aims to change the channel occupancy status from idle to busy when the
transmitter is sensing, so that the transmitter is fooled into making incorrect
transmit decisions. This attack is more energy efficient and harder to detect
compared to jamming of data transmissions. We show that this attack is very
effective and reduces the throughput of the transmitter substantially.
