摘要
对抗网络流量在设备隐私保护和网络安全等领域扮演着重要角色,然而目前对抗网络流量生成方法缺乏对质量的约束,导致生成的流量偏离原始流量特性,在实际应用中丧失其对抗能力。因此,提出一种基于GAN的对抗网络流量生成方法,改进生成器设计,以卷积神经网络提取原始流量特征的抽象表示,经基础迭代算法生成扰动,确保扰动保持原始流量的特性;优化生成器损失函数,实现生成流量与原始流量之间的最小差异;引入干扰器模块,利用网格搜索算法为扰动分配权重并优选参数组合,保证生成流量的多样性。为了综合考虑特征空间距离差异与相对变化速率对生成质量的影响,提出相对差异扰动量指标,能更准确地评估对抗网络流量与原始流量之间的差异。实验结果表明,在有效扰动范围内,相较于其他方法,该方法生成的对抗网络流量对目标分类模型保持高欺骗率的同时,产生的L∞扰动量与相对差异扰动量均更小,与原始流量的相似性更高,有效提高了对抗网络流量的生成质量。
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.
作者
杨琳
林宏刚
YANG Lin;LIN Honggang(College of Cyberspace Security,Chengdu University of Information Technology,Chengdu 610225,China;Sichuan Key Laboratory of Advanced Cryptography and System Security,Chengdu 610225,China)
出处
《计算机科学》
北大核心
2025年第S2期862-870,共9页
Computer Science
基金
国家242信息安全计划项目(2021-037)
四川省自然科学基金项目(2024NSFSC0515)。
关键词
生成对抗网络
对抗网络流量
相对差异扰动
相似性保持目标函数
生成质量控制
Generative adversarial network
Adversarial network traffic
Relative differential disturbance
Similarity preservation objective function
Generative quality control
作者简介
杨琳,born in 1999,postgra-duate.Her main research interests include adversarial network traffic gene-ration and network security.linyang_24330@163.com;通信作者:林宏刚,born in 1976,Ph.D,professor.His main research interests include cloud computing,big data security,artificial intelligence and cybersecurity.linhg@cuit.edu.cn。
