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Application of density-based outlier detection to database activity monitoring

Author

Listed:
  • Seung Kim

    (Seoul National University)

  • Nam Wook Cho

    (Seoul National University of Technology)

  • Young Joo Lee

    (Seoul National University)

  • Suk-Ho Kang

    (Seoul National University)

  • Taewan Kim

    (Somansa Inc.)

  • Hyeseon Hwang

    (Korea Atomic Energy Research Institute)

  • Dongseop Mun

    (Korea Atomic Energy Research Institute)

Abstract

To prevent internal data leakage, database activity monitoring uses software agents to analyze protocol traffic over networks and to observe local database activities. However, the large size of data obtained from database activity monitoring has presented a significant barrier to effective monitoring and analysis of database activities. In this paper, we present database activity monitoring by means of a density-based outlier detection method and a commercial database activity monitoring solution. In order to provide efficient computing of outlier detection, we exploited a kd-tree index and an Approximated k-nearest neighbors (ANN) search method. By these means, the outlier computation time could be significantly reduced. The proposed methodology was successfully applied to a very large log dataset collected from the Korea Atomic Energy Research Institute (KAERI). The results showed that the proposed method can effectively detect outliers of database activities in a shorter computation time.

Suggested Citation

  • Seung Kim & Nam Wook Cho & Young Joo Lee & Suk-Ho Kang & Taewan Kim & Hyeseon Hwang & Dongseop Mun, 2013. "Application of density-based outlier detection to database activity monitoring," Information Systems Frontiers, Springer, vol. 15(1), pages 55-65, March.
    • Handle: RePEc:spr:infosf:v:15:y:2013:i:1:d:10.1007_s10796-010-9266-9
    DOI: 10.1007/s10796-010-9266-9
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    Citations

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    Cited by:

    1. Carly L. Huth & David W. Chadwick & William R. Claycomb & Ilsun You, 2013. "Guest editorial: A brief overview of data leakage and insider threats," Information Systems Frontiers, Springer, vol. 15(1), pages 1-4, March.
    2. Himeur, Yassine & Ghanem, Khalida & Alsalemi, Abdullah & Bensaali, Faycal & Amira, Abbes, 2021. "Artificial intelligence based anomaly detection of energy consumption in buildings: A review, current trends and new perspectives," Applied Energy, Elsevier, vol. 287(C).
    3. Jihwan Lee & Nam-Wook Cho, 2016. "Fast Outlier Detection Using a Grid-Based Algorithm," PLOS ONE, Public Library of Science, vol. 11(11), pages 1-11, November.

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