  The exponential growth of internet connected systems has generated numerous
challenges, such as spectrum shortage issues, which require efficient spectrum
sharing (SS) solutions. Complicated and dynamic SS systems can be exposed to
different potential security and privacy issues, requiring protection
mechanisms to be adaptive, reliable, and scalable. Machine learning (ML) based
methods have frequently been proposed to address those issues. In this article,
we provide a comprehensive survey of the recent development of ML based SS
methods, the most critical security issues, and corresponding defense
mechanisms. In particular, we elaborate the state-of-the-art methodologies for
improving the performance of SS communication systems for various vital
aspects, including ML based cognitive radio networks (CRNs), ML based database
assisted SS networks, ML based LTE-U networks, ML based ambient backscatter
networks, and other ML based SS solutions. We also present security issues from
the physical layer and corresponding defending strategies based on ML
algorithms, including Primary User Emulation (PUE) attacks, Spectrum Sensing
Data Falsification (SSDF) attacks, jamming attacks, eavesdropping attacks, and
privacy issues. Finally, extensive discussions on open challenges for ML based
SS are also given. This comprehensive review is intended to provide the
foundation for and facilitate future studies on exploring the potential of
emerging ML for coping with increasingly complex SS and their security
problems.
