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International Conference on Knowledge Science, Engineering and Management

KSEM 2011: Knowledge Science, Engineering and Management pp 160–170Cite as

Online Internet Intrusion Detection Based on Flow Statistical Characteristics

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Part of the Lecture Notes in Computer Science book series (LNAI,volume 7091)

Abstract

Intrusion detection is one of the most essential factors for security infrastructures in network environments, and it is widely used in detecting, identifying and tracking the intruders. Traditionally, the approach taken to find attacks is to inspect the contents of every packet. An alternative approach is to detect network applications based on flow statistics characteristics using machine learning. We propose online Internet intrusion detection based on flow statistical characteristics in this paper. Experiment results illustrate this method has high detection accuracy using Seeded-Kmeans clustering algorithm. It is noticeable that the statistics of the first 12 packets could detect online flow with high accuracy.

Keywords

  • intrusion detection
  • seeded-kmeans
  • online
  • flow statistical characteristics

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  • DOI: 10.1007/978-3-642-25975-3_15
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Author information

Authors and Affiliations

  1. Institute of Information Network Technology, Nanjing University of Posts and Telecommunications, No.66 Xinmofan Road, Nanjing, Jiangsu, P.R. China

    Chengjie Gu, Shunyi Zhang & Hanhua Lu

Authors
  1. Chengjie Gu
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  2. Shunyi Zhang
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  3. Hanhua Lu
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Editor information

Editors and Affiliations

  1. Management Science and Information Systems Department, Rutgers, the State University of New Jersey, 1, Washington Park, 07102, Newark, NJ, USA

    Hui Xiong

  2. Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hong Kong, China

    W. B. Lee

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© 2011 Springer-Verlag Berlin Heidelberg

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Gu, C., Zhang, S., Lu, H. (2011). Online Internet Intrusion Detection Based on Flow Statistical Characteristics. In: Xiong, H., Lee, W.B. (eds) Knowledge Science, Engineering and Management. KSEM 2011. Lecture Notes in Computer Science(), vol 7091. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25975-3_15

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  • DOI: https://doi.org/10.1007/978-3-642-25975-3_15

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-25974-6

  • Online ISBN: 978-3-642-25975-3

  • eBook Packages: Computer ScienceComputer Science (R0)