dge by using different classification methodologies and generalizes the hypothesis. Based on the obtained knowledge, it then achieves certain goals through reasoning. The combination of SS with ML techniques has the potential to adaptively tackle complicated and dynamic allocation and classification problems such as channel selection, interference control, and resource allocation. Applying ML to different SS frameworks has become a research hotspot and a promising frontier in the field of future communications. Toward that end, this study aims to provide a timely and comprehensive survey in this up-and-coming field. 10.1.2 Security Issues in SS The development of SS techniques will help relieve spectrum scarcity. However, due to the dynamic access of spectrum resources by a variety of users, SS systems can be exposed to malicious attackers. In the first place, the lack of ownership of the spectrum leaves unlicensed users highly susceptible to malicious attacks. Therefore, it is hard to protect their opportunistic spectrum access from adversaries. In the second, the dynamic spectrum availability and distributed network structures make it challenging to implement adequate security countermeasures. Moreover, in some SS systems, PUs may contain sensitive information, which can be effortlessly obtained by malicious SUs during the SS process. Finally, new technologies such as ML may also be exploited by attackers with even more complicated and unpredictable attacks [206]. Clearly, security issues are of great concern and impose unique challenges in the SS network. According to [145], security requirements in most of the spectrum-sharing scenarios include confidentiality, integrity, availability, authentication, non-repudiation, compliance, access control, and privacy. Confidentiality means sensitive information should not be dis- closed to unauthorized users, especially in database-assisted SS systems. Integrity ensures that information communicated among users is protected from malicious alteration, insertion, deletio