基于对抗样本的网络欺骗流量生成方法  

Method to generate cyber deception traffic based on adversarial sample

作　　者：胡永进[1] 郭渊博[1] 马骏[1] 张晗[1,2] 毛秀青[1] HU Yongjin;GUO Yuanbo;MA Jun;ZHANG Han;MAO Xiuqing(Department of Cryptogram Engineering,Information Engineering University,Zhengzhou 450001,China;Software College,Zhengzhou University,Zhengzhou 450000,China)

机构地区：[1]信息工程大学密码工程学院,河南郑州450001 [2]郑州大学软件学院,河南郑州450000

出　　处：《通信学报》2020年第9期59-70,共12页Journal on Communications

基　　金：信息保障技术重点实验室开放基金资助项目(No.KJ-15-108)。

摘　　要：为了应对流量分类攻击,从防御者的角度出发,提出了一种基于对抗样本的网络欺骗流量生成方法。通过在正常的网络流量中增加扰动,形成欺骗流量的对抗样本,使攻击者在实施以深度学习模型为基础的流量分类攻击时出现分类错误,欺骗攻击者从而导致攻击失败,并造成攻击者时间和精力的消耗。采用几种不同的扰动生成方法形成网络流量对抗样本,选择LeNet-5深度卷积神经网络作为攻击者使用的流量分类模型实施欺骗,通过实验验证了所提方法的有效性,为流量混淆和欺骗提供了新的方法。In order to prevent attacker traffic classification attacks,a method for generating deception traffic based on adversarial samples from the perspective of the defender was proposed.By adding perturbation to the normal network traffic,an adversarial sample of deception traffic was formed,so that an attacker could make a misclassification when implementing a traffic analysis attack based on a deep learning model,achieving deception effect by causing the attacker to consume time and energy.Several different methods for crafting perturbation were used to generate adversarial samples of deception traffic,and the LeNet-5 deep convolutional neural network was selected as a traffic classification model for attackers to deceive.The effectiveness of the proposed method is verified by experiments,which provides a new method for network traffic obfuscation and deception.

关 键 词：对抗样本 网络流量分类 网络欺骗 网络流量混淆 深度学习 

分 类 号：TP393[自动化与计算机技术—计算机应用技术]