hes discussed above require a certain dataset to train the network. However, an unfamiliar environment and attackers may lead to classifier failure 11.2 ML-Assisted Secure SS due to an unsuitable reference model. Hence, robust detection methods are very important when adapting to the changes in the environment and more practicable in the real world. In [35], defense strategies against PUE attacks from malicious SUs were investigated. Greedy SUs manipulated unsupervised learning clustering algorithms through various attack strategies to evict other SUs from the idle channel. Corresponding countermea- sures to these manipulation attacks were developed, and the robustness of unsupervised learning for signal classification in an evolving RF environment was evaluated. By using both k-means clustering and Self-Organizing Maps (SOMs), the proposed method could perform signal classification in the absence of training data. With intentional attacks from SUs, the robustness of the classifier to avoid misclassification was verified. It was shown that the efficacy of attacks could be reduced by 75.9%. By adopting the TL algorithms, the authors in [162] developed a PUE defense approach that used knowledge about PUs and SUs from past time frames to improve the detection process in future time frames. The proposed approach extracted high-level representations of the environment and accumulated them to form an abstract knowledge database. This database enabled the CR system to accurately detect PUE attacks even if an insufficient amount of fingerprint data was available in the current time frame. The final detection decisions were used to update the abstract knowledge database for future runs. A semi-supervised distributed learning algorithm was proposed for PUE attack detection. By enabling edge devices to perform data clustering and session classification locally, it could deal efficiently with varying bandwidth, signature changes, etc. The labeled data was fed into a trained supervised learning-based classifier for classifica