<p>Deep learning-based side-channel attacks (DL-SCA) are favored for their strong key recovery capabilities. However, their implementation is based on the attacker being able to manipulate a cloned device to build an attack model, which means that the attacker needs to know secret information in advance. The non-profiled side-channel attacks (NP-SCA) methods can complete the key recovery without knowing the secret information. Differential Deep Learning Analysis (DDLA) is the first NP-DLSCA method proposed in CHES2019, and several improved versions appeared later. In these methods, the bad quality of the raw traces, such as noise, random delay, etc., is often ignored, which limits the efficiency of key recovery. In this work, the conditional generative adversarial network (CGAN) is introduced and a novel framework NPSCA-CGAN is proposed to optimize traces in non-profiled SCA scenarios. We apply CGAN in non-profiled attacks and use plaintext to do trace labeling that optimizes the raw traces by training the generator to learn the label traces. The convolutional module and plaintext feature are added to the generator network to adapt various countermeasures. Moreover, a new traces quality evaluation metric average relative signal-to-noise ratio (AR-SNR) is proposed for non-profiled attack scenarios, which can directly reflect the performance of the traces in practical attack. The method is applied to unprotected, unaligned, and masked traces respectively. The experimental results indicate that it can enormously optimize the quality of the traces and improve the efficiency of non-profiled side-channel attacks.</p>