-type scenario. To be more practical, the delay of packet exchange information between the victim senders and the receivers was considered by the 11.2 ML-Assisted Secure SS jammer, as opposed to the traditional assumption where the feedback is instantaneously available. Furthermore, to implement delayed learning in scenarios where rewards were associated with state transitions, a new method was developed. The advanced benefits of the proposed framework were verified by studying the optimal jamming strategies against an 802.11-type wireless network that used the RTS-CTS protocol to communicate and deliver information. To jam the SU communications without interfering with the PUs, a cognitive jammer with sensing capability can exploit the same statistic information and stochastic dynamic decision-making process that SUs would follow. To this end, an anti-jamming multi-channel access problem was formulated in [196] as a non-stochastic multi-armed bandit problem. By taking advantage of shared information among the transceivers, a protocol was developed that enabled SUs to selectively sense channels with a high prob- ability of non-occupancy by jammers and PUs based on the sensing and access historical information. To proactively avoid jammed channels, Q-learning was employed to learn strategies of jammers in [165, 166]. Due to the time-consuming training process required by Q learning for learning the behaviors of jammers, a wideband spectrum sensing ability was adopted to speed up the learning process. Prior learned information was also used to minimize the number of collisions with the jammer in the training phase. Finally, the effectiveness and improvement of the modified algorithm were verified. As shown in Figure 11.5, an adversarial ML approach launching jamming attacks and introducing a defense strategy was presented in [157, 163]. A transmitter T first sensed channels and identified spectrum opportunities, then transmitted data in idle channels. Samples Transmitter Labels (sensing results at T) T's classifier