Journal of Information Security Reserach ›› 2025, Vol. 11 ›› Issue (9): 807-.

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Research on Network Unknown Attack Detection Based on Machine Learning#br#
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Chen Liangchen1,2,3, Fu Deyin1, Liu Baoxu2, Lu Zhigang2, Jiang Zhengwei2, and Gao Shu3   

  1. 1(School of Computer, China University of Labor Relations, Beijing 100048)
    2(Key Laboratory of Network Assessment Technology (Institute of Information Engineering, Chinese Academy of Sciences), Beijing 100093)
    3(School of Computer and Artificial Intelligence, Wuhan University of Technology, Wuhan 430063)
  • Online:2025-09-30 Published:2025-09-30

基于机器学习的网络未知攻击检测方法研究综述

陈良臣1,2,3傅德印1刘宝旭2卢志刚2姜政伟2高曙3   

  1. 1(中国劳动关系学院计算机学院北京100048)
    2(中国科学院网络测评技术重点实验室(中国科学院信息工程研究所)北京100093)
    3(武汉理工大学计算机与人工智能学院武汉430063)
  • 通讯作者: 陈良臣 博士,副教授.主要研究方向为人工智能、机器学习、网络信息安全. chenliangchen@culr.edu.cn
  • 作者简介:陈良臣 博士,副教授.主要研究方向为人工智能、机器学习、网络信息安全. chenliangchen@culr.edu.cn 傅德印 博士,教授.主要研究方向为机器学习、统计分析、人工智能. fudeyin@culr.edu.cn 刘宝旭 博士,研究员.主要研究方向为网络攻防、安全态势感知、威胁发现. liubaoxu@iie.ac.cn 卢志刚 博士,研究员.主要研究方向为网络攻防、安全态势感知、威胁发现. luzhigang@iie.ac.cn 姜政伟 博士,正高级工程师.主要研究方向为网络攻防、安全态势感知、威胁发现. jiangzhengwei@iie.ac.cn 高曙 博士,教授.主要研究方向为人工智能、网络信息安全、计算机视觉. gshu@whut.edu.cn

Abstract: In the complex context of the continuous evolution of cybersecurity threats, the threats posed by unknown network attacks to digital infrastructure are increasing daily. Consequently, The technology for detecting unknown network attacks based on machine learning has emerged as a focal point in research. This paper first discusses the classification of intrusion detection systems and the commonly used technologies for detecting unknown network attacks. Subsequently, it conducts an indepth exploration of the methods for detecting unknown attacks based on machine learning from three dimensions: anomaly detection, openset recognition, and zeroshot learning. Furthermore, it summarizes the commonly used datasets and key evaluation indicators. Finally, it summarizes and looks ahead to the development trends and challenges of unknown attack detection. This article can provide references for further exploring new methods and technologies in the field of cyberspace security.

Key words: unknown attack detection, machine learning, anomaly detection, open set recognition, zero sample learning

摘要: 在网络安全威胁持续演变的复杂背景下,未知的网络攻击对数字基础设施的威胁与日俱增,基于机器学习的网络未知攻击检测技术成为研究重点.首先对入侵检测系统分类和网络未知攻击检测常用技术进行论述;其次从异常检测、开集识别和零样本学习3个维度对基于机器学习的网络未知攻击检测方法进行深入探讨,并进一步对常用数据集和关键评估指标进行总结;最后对未知攻击检测的发展趋势和挑战进行展望.可为进一步探索网络空间安全领域的新方法与新技术提供借鉴与参考.

关键词: 未知攻击检测, 机器学习, 异常检测, 开集识别, 零样本学习

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