ind the optimal solution without requiring prior and complete information from the environment. 10 Secure Spectrum Sharing with Machine Learning: An Overview 10.3 Summary In this chapter, we have reviewed four SS application scenarios: an opportunistic access-based CRN, a database-assisted SS, an LTE-U/LTE-LAA, and a symbiotic SS mechanism-based AmBC network. The uses of ML in approaching SS related problems with regards to the characteristics of SS frameworks were considered. Secure Spectrum Sharing with Machine Learning: Methodologies In this chapter, the security concerns of SS will be analyzed, following by some mitigation mechanisms. 11.1 Security Concerns in SS While the ML-based SS networks can help improve the SE, they can also be a double-edged sword. The dynamic access frameworks introduce more security and privacy risks into the system. As shown in Figure 11.1, when SUs observe the activity of PUs, the sensing pro- cedure can be disturbed by the malicious attackers by launching the PUE attacks or SSDF attacks. The attackers may also exploit these opportunities to harm the privacy of PUs. Besides these, the system also suffers the same security issues found in traditional wireless communications, such as jamming attacks and eavesdropping attacks. In this section, we will discuss these physical layer attacks and potential countermeasures [195]. 11.1.1 Primary User Emulation Attack In CRN, a PUE attack denotes a PU-like signals sent by an attacker during the spectrum sensing period that can exclude legitimate SU access to the channels. The attackers may be selfish users who want to use the spectrum exclusively or malicious attackers who want to disrupt the normal operation of the system. PUE attacks can cause service degradation, denial of service (DoS), connection unreliability, and bandwidth waste [223]. PUE attacks can damage required security such as availability, authentication, non-reputation, compliance, and access control [145]. Countermeasures to PUE attacks seek to enhance spectrum management. W