计算机科学与探索2026，Vol.20Issue(5)：1365-1379,15.DOI:10.3778/j.issn.1673-9418.2506007
面向网络流量特征智能混淆的通用补丁构造方法
Universal Patch Construction Method for Intelligent Obfuscation of Network Traffic Features
摘要
Abstract
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%.关键词
流量识别/对抗补丁/流量混淆/深度学习Key words
traffic identification/adversarial patch/traffic obfuscation/deep learning分类
信息技术与安全科学引用本文复制引用
奚宗棠,邢长友,张国敏,王耀辉,康梦琦..面向网络流量特征智能混淆的通用补丁构造方法[J].计算机科学与探索,2026,20(5):1365-1379,15.基金项目
国家自然科学基金面上项目(62172432) （62172432）
江苏省自然科学基金面上项目(BK20242076).This work was supported by the National Natural Science Foundation of China(62172432),and the Natural Science Foundation of Jiangsu Province(BK20242076). （BK20242076）
