基于GAN和元学习的伪装流量生成模型OA

The deep learning-based malicious traffic detection model is susceptible to adversarial attacks.In order to uncover security vulnerabilities with such models and find ways to enhance the robustness,an adversarial sample generation model(ReN-GAN)was proposed.Based on the principles of generative adversarial networks(GANs),the model could automatically generate relevant disguised traffic based on traffic features and utilize the transferability of adversarial samples to achieve black-box attacks.By intro-ducing momentum iteration methods and adding constraints on perturbations,the generalization capability of disguised traffic adversarial samples while ensuring the functionality of the original traffic was en-hanced.During training,the model was optimized by integrating meta-learning theory,enabling the tar-get integrated model to capture the common decision boundaries of various models more effectively and enhancing the transferability of generated adversarial samples.Experimental results showed that the ad-versarial samples generated by the ReN-GAN model,while preserving the characteristics of the original traffic,achieved an average evasion rate of 54.1%on black-box detection models,significantly reducing the generation time compared to other methods.Furthermore,when trained on classifiers based on DNN,the ReN-GAN model required only five iterations to generate disguised traffic with an evasion rate of 62%,greatly reducing the interaction times.