tection counter- measure. To this end, the authors in [27] proposed a DL-based jamming pattern recognition by using spectrum waterfall. In addition, the simplified Le-Net5 structure was employed to reduce the complexity of the calculation. As a result, the proposed method achieved a rapid recognition performance. By directly using temporal and spectral information like spectrum waterfall, the authors in [114] developed a novel anti-jamming approach that did not require knowledge of jamming patterns and parameters. First, a recursive CNN was designed to overcome the infinite-state issues of spectrum waterfall. Furthermore, a DRL algorithm-based anti-jamming method relying only on locally observed information was proposed to obtain optimal anti-jamming strategies. The proposed method could explore the unknown environment and combat advanced jamming attacks in a more practical fashion. A sequential DRL algorithm without prior information was proposed in [116] to defend against jamming attacks. The jamming patterns were first identified by DL and sliding window principles. Those recognized patterns were then passed to an RL-based model to inform online channel selection. To better achieve the tradeoff between throughput and overhead, channel switching cost was introduced to the system. It was shown that the proposed method could make anti-jamming channel selection decisions quickly without modeling the jammer's characteristics. 11.2.3.2 Attacker Enhanced Anti-Jamming Methods Although ML provides many effective solutions for system defense when fighting jamming attacks, it can also be exploited by attackers to develop more effective attack strategies. Considering intelligent attackers when designing defensive measures can help avoid overly clumsy assumptions and enhance protection schemes more reliable and practical. In addition to anti-jamming techniques, knowing intelligent jamming strategies is also crucial. An intelligent jammer that can adapt to its surroundings was investigated in [7] under an electronic warfare