utlier detection approaches and reputation-based approaches. In outlier detection methods, the abnormal user is excluded from the network. In reputation-based methods, on the other hand, SUs are assigned a reputation degree that Figure 11.4 Illustration of SSDF attack. 11.2 ML-Assisted Secure SS reflects their detection performance. Since SUs are not eliminated and their reports are not excluded, reputation-based methods can use the collected information more thoroughly than outlier detection techniques. 11.2.2.1 Outlier Detection Methods To mitigate SSDF attacks, the authors in [55] proposed a Support Vector Data Description (SVDD) algorithm in the sensing phase that could distinguish malicious nodes from legal ones and remove them in the decision phase. The boundary around the target data was con- structed by enclosing the target data within a minimum hyper-sphere. Enlightened by the SVM, the SVDD decision boundary was described by a few support vectors. The spectrum sensing result was then decided according to the voting results from trusted nodes. In [1], the authors designed a Bayesian nonparametric clustering scheme to sense the primary channel status and identify malicious users in CSS. By forming a single cluster consisting of all legal users and a separate cluster that included every variety of attacker (selfish, mischievous, jamming) in the feature space, their proposed approach could detect and identify multiple attacks simultaneously. Furthermore, based on observations from the collaborating CR users, it could discern malicious users from the legitimate ones and obtain the real PU traffic pattern. In [139], Bayesian learning-based SSDF defense schemes were proposed. First, a Bayesian offline learning algorithm was proposed where the spectrum state was unavailable for training. A Bayesian online learning framework was then designed by incorporating the time-varying attributes of the sensors. The framework consisted of the historical data learning part and the current data learning part. The vector of s