ion between a user and an intelligent jammer was modeled as a game in [151]. The backscat- tering time utility functions of both user and jammer were designed, and the closed-form expression for the equilibrium of the Stackelberg game was obtained. As the system SNR information and transmission strategy of the jammer were not available, Q learning was employed to obtain the optimal strategy in a dynamic iterative manner. Hot booting Q-learning was further introduced to accelerate the convergence of traditional Q learning. Most jamming countermeasures focus on how to enable users to efficiently escape the invaded channel. AmBC opens the possibility of fighting against the malicious jammer. As shown in Figure 11.6, a method that allowed wireless nodes to fight against a jamming attack instead of escaping was proposed in [85, 190]. By first learning the adversary's jamming strategy, the users could decide whether or not to adopt the rate or backscatter modulated information on the jamming signals. A dueling neural network architecture-based DRL algorithm was developed to deal with unknown jamming attacks such as jamming strategies, jamming power levels, and jamming capability. The proposed algorithm allowed the transmitter to effectively learn about the jammer and conceive optimal countermeasure actions such as adapting the transmission rate, backscattering, harvesting energy, or staying idle. The system performance in terms of learning speed, throughput, and packet loss was significantly improved by the proposed algorithm. An RL-based jamming defense method was developed in [191], where the transmitter could obtain the optimal operation policy through real-time interaction processes with the malicious attacker. To be specific, when the jammer attacked the channel, the transmit- ter could leverage the jamming signals to transmit data by using the ambient backscatter technique or harvest energy from the jamming signals to support its operation. Thus, the 11.2 ML-Assisted Secure SS Ambient RF source (e.g. TV/AM/FM rad