计算机科学 ›› 2021, Vol. 48 ›› Issue (9): 345-351.doi: 10.11896/jsjkx.200500059

• 信息安全 • 上一篇    


张师鹏, 李永忠   

  1. 江苏科技大学计算机学院 江苏 镇江212003
  • 收稿日期:2020-05-14 修回日期:2020-08-21 出版日期:2021-09-15 发布日期:2021-09-10
  • 通讯作者: 李永忠(liyongzhong61@163.com)
  • 作者简介:1099682749@qq.com
  • 基金资助:
    国家自然科学基金(61471182);江苏省研究生科研与实践创新计划项目(KYCX20_3163); 江苏省高校自然基金项目(15KJD52004)

Intrusion Detection Method Based on Denoising Autoencoder and Three-way Decisions

ZHANG Shi-peng, LI Yong-zhong   

  1. School of Computer,Jiangsu University of Science and Technology,Zhenjiang,Jiangsu 212003,China
  • Received:2020-05-14 Revised:2020-08-21 Online:2021-09-15 Published:2021-09-10
  • About author:ZHANG Shi-peng,born in 1994,postgraduate.His main research intrests include computer network security and machine learning.
    LI Yong-zhong,born in 1961,M.S,professor,M.S supervisor.His main research interests include computer network security and information security,intelligent information processing,and application of embedded system.
  • Supported by:
    National Nature Science Foundation of China(61471182),Postgraduate Research & Practice Innovation Program of Jiangsu Province(KYCX20_3163) and Natural Science Foundation of the Jiangsu Higher Education Institutions of China(15KJD52004)

摘要: 入侵检测在计算机网络安全防御中起着至关重要的作用,是网络安全的关键技术之一。随着网络环境越来越复杂,网络入侵行为也逐渐表现出了多样化及智能化的特点,且越来越难以被检测到。基于上述原因,人们对已有入侵检测方法的可行性与可持续性表示担忧,具体来说就是已有的入侵检测算法很难完美地抽象出入侵行为所包含的特征,且已有的入侵检测方法在未知攻击上大都表现不佳。针对这些问题,文中提出了基于降噪自编码器和三支决策的入侵检测算法DAE-3WD。该方法通过降噪自编码器对高维数据进行特征提取,利用多次的特征提取来构造多粒度的特征空间,然后基于三支决策理论对属于入侵或正常的行为做出立即决策,而对于疑似入侵或者疑似正常的行为则根据不同粒度的特征进行进一步的分析。深度学习具有优越的分层特征学习能力,且三支决策可以规避因信息不足而盲目分类造成的风险,该方法利用这些特性可以达到提升入侵检测表现的目的。在NSL-KDD数据集上进行了实验,实验结果证明,所提算法能提取到有意义的特征并能有效提升入侵检测算法的表现。

关键词: 入侵检测, 三支决策, 特征提取, 网络安全, 自编码器

Abstract: Intrusion detection plays a vital role in computer network security.Intrusion detection is one of the key technologies of network security and needs to be kept under constant attention.As the network environment becomes more and more complex,network intrusion behaviors gradually show diversified and intelligent characteristics,and network intrusion is also becoming more difficult to detect.And the research conducted in the field of network security is also an endless study.For the above reasons,people are worried about the feasibility and sustainability of the current method,specifically,it is difficult for current intrusion detection methods to perfectly abstract the features contained in intrusion behaviors,and most of the current intrusion detection methods perform poorly on unknown attacks.In response to these problems,we propose an intrusion detection method DAE-3WD based on denoising autoencoder and three-way decisions.We hope that our method can effectively promote the research on intrusion detection.This proposed methodextracts features from high-dimensional data through denoising autoencoder.Through multiple feature extractions,a multi-granular feature space can be constructed,and then an immediate decision on intrusive or no-rmal behavior is made based on the three-way decisions,and further analysis is required for suspected intrusion or normal beha-vior.Deep learning has superior hierarchical feature learning ability,and three-way decisions can avoid the risk of blind classification due to insufficient information.This method uses these characteristics to achieve the purpose of improving the performance of intrusion detection.The NSL-KDD data set is used in our experiments.The experiments prove that the proposed method can extract meaningful features and effectively improve the performance of intrusion detection.

Key words: Autoencoder, Feature extraction, Intrusion detection, Network security, Three-way decisions


  • TP309
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