  Security at the physical layer (PHY) is a salient research topic in wireless
systems, and machine learning (ML) is emerging as a powerful tool for providing
new data-driven security solutions. Therefore, the application of ML techniques
to the PHY security is of crucial importance in the landscape of more and more
data-driven wireless services. In this context, we first summarize the family
of bespoke ML algorithms that are eminently suitable for wireless security.
Then, we review the recent progress in ML-aided PHY security, where the term
"PHY security" is classified into two different types: i) PHY authentication
and ii) secure PHY transmission. Moreover, we treat neural networks as special
types of ML and present how to deal with PHY security optimization problems
using neural networks. Finally, we identify some major challenges and
opportunities in tackling PHY security challenges by applying carefully
tailored ML tools.
