Efficient Presentation of Multivariate Audit Data for Intrusion Detection of Web-Based Internet Services

  • Zhi Guo
  • Kwok-Yan Lam
  • Siu-Leung Chung
  • Ming Gu
  • Jia-Guang Sun
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2846)

Abstract

This paper presents an efficient implementation technique for presenting multivariate audit data needed by statistical-based intrusion detection systems. Multivariate data analysis is an important tool in statistical intrusion detection systems. Typically, multivariate statistical intrusion detection systems require visualization of the multivariate audit data in order to facilitate close inspection by security administrators during profile creation and intrusion alerts. However, when applying these intrusion detection schemes to web-based Internet applications, the space complexity of the visualization process is usually prohibiting due to the large number of resources managed by the web server. In order for the approach to be adopted effectively in practice, this paper presents an efficient technique that allows manipulation and visualization of a large amount of multivariate data. Experimental results show that our technique greatly reduces the space requirement of the visualization process, thus allowing the approach to be adopted for monitoring web-based Internet applications.

Keywords

Network security Intrusion detection Multivariate data analysis Data visualization 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Zhi Guo
    • 1
  • Kwok-Yan Lam
    • 1
  • Siu-Leung Chung
    • 2
  • Ming Gu
    • 1
  • Jia-Guang Sun
    • 1
  1. 1.School of SoftwareTsinghua UniversityBeijingPR China
  2. 2.School of Business AdministrationThe Open University of Hong Kong 

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