  Machine learning provides effective means to learn from spectrum data and
solve complex tasks involved in wireless communications. Supported by recent
advances in computational resources and algorithmic designs, deep learning has
found success in performing various wireless communication tasks such as signal
recognition and spectrum sensing. However, machine learning in general and deep
learning in particular has recently been found vulnerable to manipulations in
training and test times giving rise to a field of study called Adversarial
Machine Learning (AML). Although AML has been extensively studied in other data
domains such as computer vision and natural language processing, research for
AML in the wireless communications domain is in its early stage. This paper
presents a comprehensive review of the latest research efforts focused on AML
in wireless communications while accounting for the unique characteristics of
wireless systems. First, the necessary background on the various types of AML
attacks is provided. Then, a holistic survey of the works developing the AML
attacks and the corresponding defense mechanisms in the wireless domain is
presented. Finally, recent research trends are identified and the future
outlook for AML as a new attack surface for wireless communications is
described.
