port more devices. One of the technical challenges in the vertical SS system is how to guarantee the perfor- mance of different types of users while achieving the highest SE. To this end, the spectrum access mechanism, interference control, resource allocation, and fairness all need to be tackled in a dynamic and collaborative way. Since the concept of SS was first introduced, different SS frameworks based on various application scenarios have been developed by researchers. This chapter provides a comprehensive and systematical review on secure SS in 5G and beyond systems. The focus is more at introducing the machine learning (ML)-based methodologies, such as deep reinforcement learning. 5G and Beyond Wireless Communication Networks, First Edition. Haijian Sun, Rose Qingyang Hu, and Yi Qian. © 2024 John Wiley & Sons Ltd. Published 2024 by John Wiley & Sons Ltd. 10 Secure Spectrum Sharing with Machine Learning: An Overview 10.1.1 SS: A Brief History The concept of vertical SS was first brought up by decentralized and opportunistic cognitive radio (CR) techniques, where SUs exploited the idle spectrum with sensing ability to trans- mit their information without causing any harmful interference to the licensed PUs [132]. Traditional CR techniques enabled SUs to take advantage of spectrum opportunities by learning/monitoring the environment and adjusting their transmission parameters adap- tively. However, the opportunistic access to the bands without a license in CRN makes it challenging to guarantee the QoS and maintain a low level of interference when multiple service providers co-exist. In particular, with the increase of primary wireless devices and activities in the network, SUs may have very limited access opportunities to the spectrum resource. Therefore, new licensed spectrum access methods are needed to provide more predictable and controllable solutions to meet the high QoS requirement for both SUs and PUs [183]. In 2012, Qualcomm and Nokia initially proposed the concept of Authorized Shared Access (ASA), 