ntains a sim- ilar database for three different types of users. The difference is that the IUs in the SAS system may have very sensitive information and do not want to offer it to the database [136]. Instead, to protect the IUs, the Exclusion/Protection Zone (EZ) is applied where the SUs (PAL and GAA users) are banned from accessing the spectrum in these areas to pre- vent them from harmful interference to IUs. Environment sensing capability (ESC)-based incumbent detection also requires users in tiers 2 and 3 to perform sensing. The system then decides the spectrum access based on the EZ and ESC nodes' sensing results [14]. 10 Secure Spectrum Sharing with Machine Learning: An Overview The existing ML-based works for database-assisted SS networks are mainly focused on the EZ adjustment [71, 218], ESC performance improvement [185, 202], and spectrum access coordination [29, 182, 226, 228]. 10.2.2.1 ML-Based EZ Optimization In a database-driven SS network, even with the SS policy provided by the database, harm- ful interference still can occur between a PU and an SU due to unexpected propagation paths. In a common solution, a primary EZ is provided for the PUs that prevents SUs from using the same spectrum in EZ to keep interference at an acceptable level. However, the size of EZ needs to be optimized to efficiently cover the regions where interference may occur and a region's shape is usually not circular. In [218], an ML-based framework was developed to deal with the interference by dynam- ically adjusting the EZ. By considering the propagation characteristics and shadow fading, the framework employed under-sampling and over-sampling schemes to solve an imbal- anced data problem which can degrade the estimation accuracy of the appropriate shape of EZ. It was shown that their proposed method could significantly reduce the area of the EZ by 54% compared with the fixed circular EZ setting. Furthermore, the proposed sam- pling scheme could achieve a 1% interference probability with 21% fewer iterations and a 6% smalle