Effective Dimension in Anomaly Detection: Its Application to Computer Systems

  • Tsuyoshi Idé
  • Hisashi Kashima
Conference paper

DOI: 10.1007/978-3-540-71009-7_17

Part of the Lecture Notes in Computer Science book series (LNCS, volume 3609)
Cite this paper as:
Idé T., Kashima H. (2007) Effective Dimension in Anomaly Detection: Its Application to Computer Systems. In: Sakurai A., Hasida K., Nitta K. (eds) New Frontiers in Artificial Intelligence. Lecture Notes in Computer Science, vol 3609. Springer, Berlin, Heidelberg

Abstract

We consider the issue of online anomaly detection from a time sequence of directional data (normalized vectors) in high dimensional systems. In spite of the practical importance, little is known about anomaly detection methods for directional data. Using a novel concept of the effective dimension of the system, we successfully formulated an anomaly detection method which is free from the “curse of dimensionality.” In our method, we derive a probability distribution function (pdf) for an anomaly metric, and use a novel update algorithm for the parameters in the pdf, where the effective dimension is included as a fitting parameter. For directional data from a computer system, we demonstrate the utility of our algorithm in anomaly detection.

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Copyright information

© Springer Berlin Heidelberg 2007

Authors and Affiliations

  • Tsuyoshi Idé
    • 1
  • Hisashi Kashima
    • 1
  1. 1.IBM Research, Tokyo Research Laboratory, 1623-14 Shimotsuruma, Yamato-shi, Kanagawa 242-8502Japan

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