Spectrum Data Poisoning with Adversarial Deep Learning

Machine learning has been applied in wireless communications. In this paper, we consider the case that a cognitive transmitter senses the spectrum and transmits on idle channels determined by a machine learning algorithm. We then present an adversarial machine learning approach to launch a spectrum data poisoning attack. That is, an adversary learns the transmitter's behavior and attempts to falsify the spectrum sensing data over the air by transmitting 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 also 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.