对抗机器学习在网络入侵检测领域的应用

刘奇旭; 王君楠; 尹捷; 陈艳辉; 刘嘉熹

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

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对抗机器学习在网络入侵检测领域的应用

专题：计算机通信与网络系统安全技术 | 更新时间：2024-06-05

- 对抗机器学习在网络入侵检测领域的应用
- Application of adversarial machine learning in network intrusion detection
- 通信学报 2021年42卷第11期页码：1-12
- 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：
  
  [ "刘奇旭（1984− ），男，江苏徐州人，博士，中国科学院信息工程研究所研究员，中国科学院大学教授，主要研究方向为网络攻防技术、网络安全评测" ]
  [ "王君楠（1995− ），女，吉林省吉林市人，中国科学院大学博士生，主要研究方向为机器学习、机器学习安全和恶意流量检测" ]
  [ "尹捷（1991− ），女，重庆人，博士，中国科学院信息工程研究所工程师，主要研究方向为网络攻防技术、恶意代码分析" ]
  [ "陈艳辉（1996− ），男，山东潍坊人，中国科学院大学博士生，主要研究方向为网络攻防技术和恶意软件分析与检测" ]
  [ "刘嘉熹（1997− ），女，山东淄博人，中国科学院大学博士生，主要研究方向为恶意代码分析" ]
- 基金信息：
  
  中国科学院青年创新促进会基金资助项目(2019163);国家自然科学基金资助项目(61902396);中国科学院战略性先导科技专项基金资助项目(XDC02040100);中国科学院网络测评技术重点实验室和网络安全防护技术北京市重点实验室基金资助项目
- DOI：10.11959/j.issn.1000-436x.2021193
  中图分类号： TN92
- 网络首发：2021-11，
  
  纸质出版：2021-11-25
- 稿件说明：
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刘奇旭, 王君楠, 尹捷, 等. 对抗机器学习在网络入侵检测领域的应用[J]. 通信学报, 2021,42(11):1-12.

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.
刘奇旭, 王君楠, 尹捷, 等. 对抗机器学习在网络入侵检测领域的应用[J]. 通信学报, 2021,42(11):1-12. DOI： 10.11959/j.issn.1000-436x.2021193.

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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