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International Conference on Ubiquitous Intelligence and Computing

UIC 2006: Ubiquitous Intelligence and Computing pp 826–834Cite as

Incorporating Data Mining Tools into a New Hybrid-IDS to Detect Known and Unknown Attacks

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 4159))

Abstract

Modern network attacks range from fully automated to multilayer attacks. Vulnerabilities in a system are exploited by an intelligent attacker to facilitate to do anything from denial of service (DoS) attacks to the system takeover. This paper addresses the development of an architecture that includes the use of fault tolerance and honeypot technology to provide layered protection to avoid a single point of failure.

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References

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Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, La Trobe University, Bundoora Campus, Melbourne, 3083, Australia

    Lokesh D. Pathak & Ben Soh

Authors
  1. Lokesh D. Pathak
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  2. Ben Soh
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Editor information

Editors and Affiliations

  1. Hosei University, 184-8584, Tokyo, Japan

    Jianhua Ma

  2. Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology, Huazhong University of Science and Technology, 430074, Wuhan, China

    Hai Jin

  3. Department of Computer Science, St. Francis Xavier University, Antigonish, Canada

    Laurence T. Yang

  4. Department of Biotechnology and Bioinformatics, Asia University, No. 500, Lioufeng Road, Wufeng Shiang, 413, Taichung, Taiwan

    Jeffrey J.-P. Tsai

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© 2006 Springer-Verlag Berlin Heidelberg

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Cite this paper

Pathak, L.D., Soh, B. (2006). Incorporating Data Mining Tools into a New Hybrid-IDS to Detect Known and Unknown Attacks. In: Ma, J., Jin, H., Yang, L.T., Tsai, J.JP. (eds) Ubiquitous Intelligence and Computing. UIC 2006. Lecture Notes in Computer Science, vol 4159. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11833529_84

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  • DOI: https://doi.org/10.1007/11833529_84

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-38091-7

  • Online ISBN: 978-3-540-38092-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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