malicious behaviors early on. Furthermore, ML can also be used to intelligently predict new attacks, which often are the mutations of previous attacks by exploring the existing records. Consequently, the SS network must transition from merely facilitating secure communication to security-based intelligence enabled by ML for effective and secure systems. The state-of-the-art learning-based security solutions for SS systems will also be comprehensively reviewed in this chapter. ML-Based Methodologies for SS In this section, key processes in the CRN network are first introduced, which are shared by most spectrum frameworks. Summarizing the roles that ML plays in these processes can help us to better understand the combination of ML and other frameworks. It also allows us to better assess the security risks in existing SS frameworks with a comprehensive understanding of the mechanism behind each SS technique. The special problems faced by database-assisted SS frameworks (such as LSA and CBRS) are then investigated, i.e. how to protect IUs when unlicensed users are introduced into licensed spectrum bands. Next, a discussion of the application of ML to the coexistence of licensed LTE systems and unli- censed WiFi systems in unlicensed frequency bands is presented. Finally, a comprehensive study of the AmBC system using ML is conducted to gain insight into the symbiosis-based SS framework as well as the benefits of the combination of AmBC and CRN. The content structure is as illustrated in Figure 10.2. 10.2.1 ML-Based CRN In CRN, unlicensed SUs need to identify the vacant or unoccupied licensed frequency band (spectrum hole) owned by licensed PUs [73]. After spotting the spectrum hole, SUs can access it without visibly interfering with any PU. If a PU's activity reappears, the SUs must vacate the spectrum immediately. This dynamic and uncertain environment creates unique and complex challenges within the CRN. However, ML algorithms are very effective in dealing with such challenges and can help improve system performanc