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Sampling Distance Analysis of Gigantic Data Mining for Intrusion Detection Systems

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Computational Intelligence and Security (CIS 2005)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 3802))

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Abstract

Real-Time intrusion detection system (IDS) based on traffic analysis is one of the highlighted topics of network security researches. Restricted by computer resources, real-time IDS is computationally infeasible to deal with gigantic operations of data storage and analyzing in real world. As a result, the sampling measurement technique in a high-speed network becomes an important issue in this topic. Sampling distance analysis of gigantic data mining for IDS is shown in this paper. Based on differential equation theory, a quantitative analysis of the effect of IDS on the network traffic is given firstly. Secondly, a minimum delay time of IDS needed to detect some kinds of intrusions is analyzed. Finally, an upper bound of the sampling distance is discussed. Proofs are given to show the efficiency of our approach.

This research was partially supported by National Natural Science Foundation of China (No.90204012) and Hi-Tech Research and Development Program of China (No.2002AA143021).

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References

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Author information

Authors and Affiliations

  1. Key Laboratory of Computer Networks and Information Security, Ministry of EducationXidian University, Xi’an, 710071, China

    Yong Zeng & Jianfeng Ma

Authors
  1. Yong Zeng
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  2. Jianfeng Ma
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Editor information

Editors and Affiliations

  1. Microelectronic Instiute, Xidian University, 710071, Xi’an, China

    Yue Hao

  2. Department of Computer Science, Hong Kong Baptist University, Kowloon Tong, Hong Kong

    Jiming Liu

  3. School of Computer Science and Technology, Xidian University, Xi’an, China

    Yu-Ping Wang

  4. Department of Computer Science, Hong Kong Baptist University, Hong Kong,  

    Yiu-ming Cheung

  5. School of Electrical and Electronic Engineering, University of Manchester, UK

    Hujun Yin

  6. Life Science Research Center, School of Electronic Engineering, Xidian University, 710071, Xi’an, Shaanxi, China

    Licheng Jiao

  7. Key Laboratory of Computer Networks and Information Security(Ministry of Education), Xidian University, 710071, Xi’an, China

    Jianfeng Ma

  8. National Laboratory of Antennas and Microwave Technology, Xidian University, 710071, Xi’an, Shanxi, P.R. China

    Yong-Chang Jiao

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© 2005 Springer-Verlag Berlin Heidelberg

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Zeng, Y., Ma, J. (2005). Sampling Distance Analysis of Gigantic Data Mining for Intrusion Detection Systems. In: Hao, Y., et al. Computational Intelligence and Security. CIS 2005. Lecture Notes in Computer Science(), vol 3802. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11596981_34

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  • DOI: https://doi.org/10.1007/11596981_34

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-30819-5

  • Online ISBN: 978-3-540-31598-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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