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Detecting Novel Network Attacks with a Data Field

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Intelligence and Security Informatics (WISI 2006)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 3917))

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Abstract

With the increased usage of computer networks, network intrusions have greatly threatened the Internet infrastructures. Traditional signature-based intrusion detection often suffers from an ineffectivity to those previously “unseen” attacks. In this paper, we analyze the network intrusions from a new viewpoint based on data field and propose branch and bound tree to lessen computation complexity. Finally, we evaluated our approach over KDD Cup 1999 data set.

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Author information

Authors and Affiliations

  1. Software Department, Inst. of Computing Tech., Chinese Academy of Science, Beijing, 100080, P.R. China

    Feng Xie & Shuo Bai

  2. Graduate School, Chinese Academy of Science, Beijing, P.R. China

    Feng Xie

Authors
  1. Feng Xie
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  2. Shuo Bai
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Editor information

Editors and Affiliations

  1. Department of Management Information Systems, Eller College of Management, The University of Arizona, 85721, AZ, USA

    Hsinchun Chen

  2. Chinese Academy of Sciences, 100190, Beijing, China

    Fei-Yue Wang

  3. Drexel University, 19104, Philadelphia, PA, USA

    Christopher C. Yang

  4. MIS Department, University of Arizona, and Chinese Academy of Sciences, Tucson, AZ, USA

    Daniel Zeng

  5. The University of Hong Kong, Hong Kong, China

    Michael Chau

  6. School of Computer Engineering, Nanyang Technological University, 639798, Singapore

    Kuiyu Chang

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© 2006 Springer-Verlag Berlin Heidelberg

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Xie, F., Bai, S. (2006). Detecting Novel Network Attacks with a Data Field. In: Chen, H., Wang, FY., Yang, C.C., Zeng, D., Chau, M., Chang, K. (eds) Intelligence and Security Informatics. WISI 2006. Lecture Notes in Computer Science, vol 3917. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11734628_8

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  • DOI: https://doi.org/10.1007/11734628_8

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-33361-6

  • Online ISBN: 978-3-540-33362-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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