Application of adversarial machine learning in network intrusion detection

Qixu LIU; Junnan WANG; Jie YIN; Yanhui CHEN; Jiaxi LIU

doi:10.11959/j.issn.1000-436x.2021193

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Application of adversarial machine learning in network intrusion detection

Topics: New Technology of Computer Communication and Network System Security | 更新时间：2024-06-05

- Application of adversarial machine learning in network intrusion detection
- Journal on Communications Vol. 42, Issue 11, Pages: 1-12(2021)
- 作者机构：
  
  1. 中国科学院信息工程研究所，北京 100093
  2. 中国科学院大学网络空间安全学院，北京 100049
- 作者简介：
- 基金信息：
  
  The Youth Innovation Promotion Association CAS(2019163);The National Natural Science Foundation of China(61902396);The Strategic Priority Research Program of Chinese Academy of Sciences(XDC02040100);The Key Laboratory of Network Assessment Technology at Chinese Academy of Sciences and Beijing Key Laboratory of Network Security and Protection Technology
- DOI：10.11959/j.issn.1000-436x.2021193
  CLC： TN92
- Online First：2021-11，
  
  Published：25 November 2021
- 稿件说明：
移动端阅览
Qixu LIU, Junnan WANG, Jie YIN, et al. Application of adversarial machine learning in network intrusion detection[J]. Journal on Communications, 2021, 42(11): 1-12.
DOI：

Qixu LIU, Junnan WANG, Jie YIN, et al. Application of adversarial machine learning in network intrusion detection[J]. Journal on Communications, 2021, 42(11): 1-12. DOI： 10.11959/j.issn.1000-436x.2021193.

摘要

近年来，机器学习技术逐渐成为主流网络入侵检测方案。然而机器学习模型固有的安全脆弱性，使其难以抵抗对抗攻击，即通过在输入中施加细微扰动而使模型得出错误结果。对抗机器学习已经在图像识别领域进行了广泛的研究，在具有高对抗性的入侵检测领域中，对抗机器学习将使网络安全面临更严峻的安全威胁。为应对此类威胁，从攻击、防御2个角度，系统分析并整理了将对抗机器学习技术应用于入侵检测场景的最新工作成果。首先，揭示了在入侵检测领域应用对抗机器学习技术所具有的独特约束和挑战；其次，根据对抗攻击阶段提出了一个多维分类法，并以此为依据对比和整理了现有研究成果；最后，在总结应用现状的基础上，讨论未来的发展方向。

Abstract

In recent years

machine learning (ML) has become the mainstream network intrusion detection system(NIDS).However

the inherent vulnerabilities of machine learning make it difficult to resist adversarial attacks

which can mislead the models by adding subtle perturbations to the input sample.Adversarial machine learning (AML) has been extensively studied in image recognition.In the field of intrusion detection

which is inherently highly antagonistic

it may directly make ML-based detectors unavailable and cause significant property damage.To deal with such threats

the latest work of applying AML technology was systematically investigated in NIDS from two perspectives: attack and defense.First

the unique constraints and challenges were revealed when applying AML technology in the NIDS field; secondly

a multi-dimensional taxonomy was proposed according to the adversarial attack stage

and current work was compared and summarized on this basis; finally

the future research directions was discussed.

关键词

Keywords

references

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