面向网络流量特征智能混淆的通用补丁构造方法OA

Deep learning-based encrypted network traffic identification technologies may lead to the leakage of sensitive network information.Existing adversarial sample defense schemes generally face challenges such as high bandwidth over-head,poor adaptability in black-box scenarios,and poor generality of defense methods.To address these issues,this paper proposes a universal patch construction method(UPCM)for intelligent obfuscation of network traffic features,based on a feature-reversible traffic representation graph,to construct an offline,universal,and undirected adversarial patch generation framework for confusing traffic at both the feature level and packet level.A Gaussian noise-driven adaptive perturbation strategy is designed to support intelligent obfuscation of the temporal features of network traffic.Experiments on real-world network traffic datasets show that UPCM achieves a defense success rate of over 85%in typical network traffic en-vironments,with bandwidth overhead controlled within 10%.Furthermore,the adversarial patches generated by UPCM exhibit strong generality and transferability:a single patch can defend against all types of network traffic,and when mi-grated to other deep learning models based on temporal features,the defense success rate remains approximately 85%.