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Anomaly Detection with Changing Cluster Centers

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Advanced Data Mining and Applications (ADMA 2018)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11323))

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Abstract

In view of the complexity of information system environment and the diversity of security requirements, many scholars proposed intrusion detection methods based on outlier mining. In order to meet the security requirement of condition guarantee information system, this paper proposes an anomaly detection with changing cluster centers (ADCCC). The rough set algorithm is used to reduce the sample set, and the number of sample repeats is determined on the basis of the duplicated degrees. The algorithm determines whether the sample is an outlier sample, mainly by changing the cluster center before and after adding a sample. Based on the overall deviation degree of the sample set, we can determine whether the sample set is an anomaly sample. Experimental results show that ADCCC algorithm has higher detection rate for anomaly detection.

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Authors and Affiliations

  1. Nanjing University of Aeronautics and Astronautics, Nanjing, 210016, China

    Zhang Peng & Zhou Liang

Authors
  1. Zhang Peng
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  2. Zhou Liang
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Corresponding authors

Correspondence to Zhang Peng or Zhou Liang .

Editor information

Editors and Affiliations

  1. University of Connecticut, Storrs, CT, USA

    Guojun Gan

  2. Nanjing University of Aeronautics and Astronautics, Nanjing, China

    Bohan Li

  3. The University of Queensland, Brisbane, QLD, Australia

    Xue Li

  4. Beijing Institute of Technology, Beijing, China

    Shuliang Wang

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Peng, Z., Liang, Z. (2018). Anomaly Detection with Changing Cluster Centers. In: Gan, G., Li, B., Li, X., Wang, S. (eds) Advanced Data Mining and Applications. ADMA 2018. Lecture Notes in Computer Science(), vol 11323. Springer, Cham. https://doi.org/10.1007/978-3-030-05090-0_4

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  • DOI: https://doi.org/10.1007/978-3-030-05090-0_4

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-05089-4

  • Online ISBN: 978-3-030-05090-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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