ness of the proposed ensemble method was verified compared with other benchmarks. An SVM-based scheme was proposed in [240] to deal with SSDF attacks. SUs' behaviors were analyzed from multi-round records of energy values, and their classification accuracy was obtained. Furthermore, the concepts of recognition probability and misclassification probability were introduced, and the tradeoff relationship between misclassification probability and threshold of classification accuracy was obtained. As a result, the proposed scheme enabled excellent adaptability for Malicious SU (MSU) detection in various scenarios. 11.2.2.3 SSDF and PUE Combination Attacks The combination of the SSDF attack and the PUE attack presents more challenges to the network. If SUs are be attacked or mislead by the PUE attack, the performance of exist- ing SSDF defense methods is degraded. Even if the PUE attacker is detected, neighboring SUs can still submit flawed sensing reports due to the contaminated signal from the PUE attack. Investigating the combination of attacks and corresponding ML-based countermea- sures in existing works can provide a comprehensive understanding of secure design in SS networks. Secure sensing under both PUE and SSDF attacks in CRN was investigated in [99]. Directly excluding attacked SUs from the sensing cooperation process requires lots of information, i.e. the attack strength, geographical locations of SUs, etc. Therefore, a novel secure sensing algorithm was developed to deal with the problem. To be specific, Unsu- pervised ML (UML) was adopted to identify contaminated sensing reports from trusted users by examining their sensing history. These contaminated sensing reports were then excluded from CSS. Moreover, considering that identification errors might occur during the UML process, each SU was assigned an identity value to account for its reliability. The identity value was also used to alleviate the misidentification impact on real trusted users. To defend against various malicious attacks and interference 