 generator and discriminator of each GAN model became smarter and smarter. It was shown that discrimi- nators were able to detect about 50% of PUE attackers without the GAN training during the deployment phase, and both the GAN models could achieve 100% accuracy during the train- ing phase. After the GAN training, the discriminators of the dumb generator could achieve 98% accuracy while the smart generator-based model could achieve 99.5% accuracy. Optimal PUE attack strategies were investigated in [37], where prior knowledge on PU activity characteristics and SU access strategies was not available. Based on previous attacking experience, a non-stochastic online learning problem was formulated to deter- mine attacking channel decisions for attackers. Since a PUE attacker never knows if an SU has ever tried to access the attacked channel or not, it cannot observe the reward. Therefore, an Attack-But-Observe-Another (ABOA) scheme was proposed to solve this issue. The attackers in this scheme attack one channel in the spectrum sensing phase but observe one or more other channels in the data transmission phase. Two non-stochastic online learning-based attacking algorithms, EXP3-DO and OPT-RO, were proposed to select the observing channel deterministically based on the attacking channel and uniform randomly, respectively. 11.2.2 State-of-the-Art Methods of Defense Against SSDF Attack CSS can help overcome the fading environments and improve the system sensing per- formance. Different from single-user-based SS, each SU needs to transmit the sensing results to a Fusion Center (FC) in CSS. FC then combines those results and makes a final decision about the PU's presence. SSDF is the most common attack in CSS. As shown in Figure 11.4, sending falsified sensing data to the FC can lead to an incorrect fusion result, cause interference with PUs, and cause DoS to SUs. To defend against SSDF attacks, the most important step is to differentiate attackers from legal SUs. The existing defense methods fall into two groups, namely o