计算机科学 ›› 2022, Vol. 49 ›› Issue (11A): 211000039-11.doi: 10.11896/jsjkx.211000039

• 信息安全 • 上一篇    下一篇

对抗性网络流量的生成与应用综述

王珏, 芦斌, 祝跃飞   

  1. 信息工程大学网络空间安全学院 郑州 450001
    数学工程与先进计算国家重点实验室 郑州 450001
  • 出版日期:2022-11-10 发布日期:2022-11-21
  • 通讯作者: 祝跃飞(yfzhu17@sina.com)
  • 作者简介:(1152808097@qq.com)
  • 基金资助:
    国家重点研发计划前沿科技创新专项基金(2019QY1300)

Generation and Application of Adversarial Network Traffic:A Survey

WANG Jue, LU Bin, ZHU Yue-fei   

  1. School of Cyberspace Security,Information Engineering University,Zhengzhou 450001,China
    State Key Laboratory of Mathematical Engineering and Advanced Computing,Zhengzhou 450001,China
  • Online:2022-11-10 Published:2022-11-21
  • About author:WANG Jue,born in 1998,postgraduate.His main research interests include cybersecurity and intrusion detection.
    ZHU Yue-fei,born in 1962,Ph.D,professor,Ph.D supervisor.His main research interests include cryptography,intrusion detection and information security.
  • Supported by:
    Cutting-edge Science and Technology Innovation Project of the Key R & D Program of China(2019QY1300).

摘要: 人工智能技术的井喷式发展正在深刻影响着网络空间安全的战略格局,在入侵检测领域显示出了巨大潜力。最近的研究发现,机器学习模型有着严重的脆弱性,针对该脆弱性衍生的对抗样本通过在原始样本上添加一些轻微扰动就可以大幅度降低模型检测的正确率。学术界已经在图像分类领域对对抗性图片的生成与应用进行了广泛而深入的研究。但是,在入侵检测领域,对于对抗性网络流量的探索仍在不断发展。在介绍对抗性网络流量的基本概念、威胁模型与评价指标的基础上,对近年来有关对抗性网络流量的研究工作进行了总结,按照其生成方式与原理的不同将生成方法分为5类:基于梯度的生成方法、基于优化的生成方法、基于GAN的生成方法、基于决策的生成方法以及基于迁移的生成方法。通过对相关问题的讨论,就该技术的发展趋势进行了展望。

关键词: 网络安全, 入侵检测, 机器学习, 对抗样本

Abstract: The spurt of artificial intelligence technology is profoundly affecting the strategic landscape of cyberspace security,showing great potential in the field of intrusion detection.Recent research finds that machine learning models have severe vulnerabilities,and the adversarial samples derived from this vulnerability can significantly reduce the correctness of model detection by adding some minor perturbations to the original samples.The generation and application of adversarial images has been extensively and intensively studied by academics in the field of computer vision.However,in the field of intrusion detection,the exploration of adversarial network traffic continues to evolve.Based on an introduction to the basic concepts,threat models and evaluation metrics of adversarial network traffic,the research works on adversarial network traffic in recent years are summarized,and the generation methods are classified into five categories according to their generation methods and principles:1) gradient-based generation method;2) optimization-based generation method;3)GAN-based generation method;4) decision-based generation me-thod;5) migration-based generation method.Through the discussion of related issues,an outlook on the development trend of this technology is presented.

Key words: Cybersecurity, Intrusion detection, Machine learning, Adversarial sample

中图分类号: 

  • TP393
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