hen defending against PUE attacks, it is impor- tant to differentiate between malicious users and legitimate users. This can be determined by their location, received signal strength, received signal power, and other features. 11.1.2 Spectrum Sensing Data Falsification Attack CSS as one promising approach for PUs' activities detection involves exploiting the spatial location diversity of multiple SUs. A group of SUs collaborate to perform the spectrum sensing by exchanging locally collected information. An SSDF attack (also known as the 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. 11 Secure Spectrum Sharing with Machine Learning: Methodologies Incumbent Privacy Transmission Eavesdropping Jamming Access Access Transmission attacker controller attacker Eavesdropping Jamming Second Transmission Figure 11.1 Secure issues in SS network. Byzantine attack) is launched in CSS by sending false local spectrum sensing results to others, leading to flawed spectrum sensing decisions [222]. SSDF attacks aim to decrease detection probability and disturb normal operations of the primary system. It may also seek to increase the probability of false alarms in order to deprive honest SUs of access opportunities [227]. SSDF attacks harm the system's integrity and availability. SSDF attackers can be classified into three types: selfish SSDF, interference SSDF, and confusing SSDF [222]. (1) A selfish SSDF attacker seeks to gain exclusive access to the target spectrum. It falsely reports the existence of relatively high PU activities to block other SUs from using the spectrum when the PU does not exist. (2) An interference SSDF attacker falsely reports low PU activities leading other SUs to wrongly conclude that they can use the spectrum without interfering with any PUs. This type of attack seeks to either cause the inference to the PU or inhibit the communication of other SUs. (3) A confusing SSDF attacke