Abstract: Adversarial network traffic plays a crucial role in fields such as device privacy protection and network security.How-ever,current adversarial network traffic generation methods lack constraints on quality,resulting in generated traffic that deviates from the original traffic characteristics,thereby losing its adversarial capability in practical applications.Therefore,this paper proposes a GAN-based adversarial network traffic generation method,which improves the generator design.The convolutional neural network is employed to extract abstract representations of original traffic features,and perturbations are generated through basic iterative algorithms to ensure that the perturbations maintain the characteristics of the original traffic.The generator loss function is optimized to achieve minimal differences between the generated traffic and the original traffic.Additionally,a perturber module is introduced,utilizing a grid search algorithm to assign weights to perturbations and optimize parameter combinations,ensuring the diversity of the generated traffic.To comprehensively consider the impact of feature space distance differences and relative change rates on generation quality,a relative difference disturbance metric is proposed to more accurately evaluate the differences between adversarial network traffic and the original traffic.Experimental results show that,within an effective perturbation range,compared to other methods,the adversarial network traffic generated by this method maintains a high deception rate for target classification models while producing smaller L_∞ disturbance and relative difference disturbance values,and exhibiting higher similarity to the original traffic,effectively improving the generation quality of adversarial network traffic.

Key words: Generative adversarial network, Adversarial network traffic, Relative differential disturbance, Similarity preservation objective function, Generative quality control