  Machine learning finds rich applications in Internet of Things (IoT) networks
such as information retrieval, traffic management, spectrum sensing, and signal
authentication. While there is a surge of interest to understand the security
issues of machine learning, their implications have not been understood yet for
wireless applications such as those in IoT systems that are susceptible to
various attacks due the open and broadcast nature of wireless communications.
To support IoT systems with heterogeneous devices of different priorities, we
present new techniques built upon adversarial machine learning and apply them
to three types of over-the-air (OTA) wireless attacks, namely jamming, spectrum
poisoning, and priority violation attacks. By observing the spectrum, the
adversary starts with an exploratory attack to infer the channel access
algorithm of an IoT transmitter by building a deep neural network classifier
that predicts the transmission outcomes. Based on these prediction results, the
wireless attack continues to either jam data transmissions or manipulate
sensing results over the air (by transmitting during the sensing phase) to fool
the transmitter into making wrong transmit decisions in the test phase
(corresponding to an evasion attack). When the IoT transmitter collects sensing
results as training data to retrain its channel access algorithm, the adversary
launches a causative attack to manipulate the input data to the transmitter
over the air. We show that these attacks with different levels of energy
consumption and stealthiness lead to significant loss in throughput and success
ratio in wireless communications for IoT systems. Then we introduce a defense
mechanism that systematically increases the uncertainty of the adversary at the
inference stage and improves the performance. Results provide new insights on
how to attack and defend IoT networks using deep learning.
