tion. Based on the error rate, it adjusted the training vectors and improved the overall performance. It was shown that the proposed method could significantly reduce false alarms in the secondary network and improve overall detection accuracy in the primary network. In [125], an adaptive Bayesian learning automaton algorithm-based scheme named Multi-channel Bayesian Learning Automata (MBLA) was proposed to defend against PUE attackers. The SU in the considered model adopted Uncoordinated Frequency Hopping (UFH) to avoid PUE attacks. To improve the speed and accuracy of the learning process in non-stationary environments, MBLA utilized two different channels simultaneously to perform the optimal frequency channel selections. Statistical information about channels and PUs was assumed unavailable. An SU synchronized with its receiver and sent its data on various channels obtained by the MBLA. The scheme extracted the best strategies for the attacker and the SU and then evaluated the proposed scheme in terms of the SU throughput in the presence of the PUE attacker. 11.2.1.3 ML-Based Attack Methods Throughout the above discussions, various PUE attack detection and defense strategies have been offered. However, the best-attack strategies have not been discussed. A better understanding of optimal attack strategies can enable researchers to quantify the sever- ity or impact of a PUE attacker on an SU's throughput. It can also shed light on practical defense strategy design as the attackers can also exploit ML algorithms to improve their performance. In [156], the authors presented two GAN-based models that successfully emulated the PUs. Depending on whether any prior knowledge of the PU's feature space was available, 11 Secure Spectrum Sharing with Machine Learning: Methodologies they proposed a dumb generator model and a smart generator model. Two DNN-based dis- criminator models were developed to distinguish the PU and the Emulated PU (EPU) from the corresponding generators. With iterative and sequential training, the