in full-duplex CRNs (FD-CRNs), an ensemble ML (EML)-based robust CSS framework was proposed in [231, 232]. SUs were assumed to have the ability to sense and transmit over the same frequency band simultaneously. The self-interference and co-channel interference were inevitably introduced into the system and complicated the sensing environment. By investigating spectrum waste probability, collision probability, and secondary throughput in both FD LBT and Listen-and-talk protocols, the robust and accurate fusion performance of the proposed EML approach was verified. 11.2.3 State-of-the-Art Methods of Defense Against Jamming Attacks A jamming attack is a common in wireless communication systems and there have been many works on this subject. Due to the nature of SS, jamming attack is effective, hence can cause severe damages. In the following, we review latest advances in anti-jamming with ML. 11.2 ML-Assisted Secure SS 11.2.3.1 ML-Based Anti-Jamming Methods In the SS network, except for the very rapid change of dynamic spectrum characteristics in the channel, the inclusion of random jammers makes efficient communication more challenging. To defend against jamming attacks, the most common countermeasure strategies are dynamic channel assignments based on different ML algorithms. The jamming attack sce- nario can be modeled by using the stochastic zero-sum game and MDP framework. The time-varying characteristics of the channel as well as the jammer's random strategy can be learned by the SU using RL algorithms. A stochastic game framework was proposed in [193] for anti-jamming defense. At each stage of the game, SUs observe the spectrum availability, the channel quality, and the attackers' strategy from the status of jammed channels. Based on observation results, the number of reserved channels and the channel switch action policy are decided. SUs employ Minimax-Q learning to learn the optimal policy, maximizing the expected sum of discounted spectrum-efficient throughput. It was shown the proposed stationary policy i