n, or replay. Availability assures users access to the spectrum/database when it is required. Authentication requires that the users should be able to establish and verify their identity. Non-repudiation means users should be able to deny having received/sent a message or to deny having accessed the spectrum at a specified location and time. Compliance means the network should be able to detect non-compliant behavior that results in harmful interference. Access control indicates that users should not access the spectrum/database without credentials. Privacy means users' sensitive/private information should be protected. Diverse security threats in different network layers can prevent the SS system from meet- ing the above requirements. In this chapter, we will mainly focus on the threats and mitiga- tion strategies in the physical layer of the SS network. We investigate works related to two classical spectrum sensing attacks in the SS network, i.e. PUE attacks and SSDF attacks, which aim to disturb the spectrum observation and users' access to the system. We also studied methods of preventing two attacks that commonly exist in wireless communica- tion networks, i.e. jamming attacks and eavesdropping attacks. The special features of the 10.2 ML-Based Methodologies for SS SS network provide new defense solutions for these common attacks. Since PUs need to open their exclusive license spectrum to coexist with multi-type users in some SS frame- works, we also investigated privacy issues and corresponding countermeasures To further enhance the security performance of the SS system, ML has become an important part of security and privacy protections in various applications. ML is a powerful tool for data exploration and can distinguish normal and abnormal behaviors based on how devices in the SS system interact with each other during spectrum access. The behavioral data of each component in the SS network can be collected and analyzed to determine normal patterns of interaction, thereby allowing the system to identify 