基于对抗样本的网络欺骗流量生成方法

胡永进; 郭渊博; 马骏; 张晗; 毛秀青

doi:10.11959/j.issn.1000-436x.2020166

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基于对抗样本的网络欺骗流量生成方法

学术论文 | 更新时间：2024-06-05

- 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
- 通信学报 2020年41卷第9期页码：59-70
- 作者机构：
  
  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
- 作者简介：
  
  [ "胡永进（1981- ），男，山东潍坊人，信息工程大学讲师，主要研究方向为主动防御、态势感知" ]
  [ "郭渊博（1975- ），男，陕西周至人，博士，信息工程大学教授、博士生导师，主要研究方向为大数据安全、态势感知" ]
  [ "马骏（1981- ），男，山西阳泉人，博士，信息工程大学副教授，主要研究方向为态势感知与威胁发现" ]
  [ "张晗（1985- ），女，河南项城人，信息工程大学博士生，主要研究方向为自然语言处理、信息安全" ]
  [ "毛秀青（1980- ），男，安徽滁州人，信息工程大学副教授，主要研究方向为人工智能安全" ]
- 基金信息：
  
  信息保障技术重点实验室开放基金资助项目(KJ-15-108)
- DOI：10.11959/j.issn.1000-436x.2020166
  中图分类号： TP393
- 网络出版日期：2020-09，
  
  纸质出版日期：2020-09-25
- 稿件说明：
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胡永进, 郭渊博, 马骏, 等. 基于对抗样本的网络欺骗流量生成方法[J]. 通信学报, 2020,41(9):59-70.

Yongjin HU, Yuanbo GUO, Jun MA, et al. Method to generate cyber deception traffic based on adversarial sample[J]. Journal on communications, 2020, 41(9): 59-70.
胡永进, 郭渊博, 马骏, 等. 基于对抗样本的网络欺骗流量生成方法[J]. 通信学报, 2020,41(9):59-70. DOI： 10.11959/j.issn.1000-436x.2020166.

Yongjin HU, Yuanbo GUO, Jun MA, et al. Method to generate cyber deception traffic based on adversarial sample[J]. Journal on communications, 2020, 41(9): 59-70. DOI： 10.11959/j.issn.1000-436x.2020166.

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。

Abstract

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.

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Keywords

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