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Self-adaptive Change Detection in Streaming Data with Non-stationary Distribution

  • Xiangliang Zhang
  • Wei Wang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6440)

Abstract

Non-stationary distribution, in which the data distribution evolves over time, is a common issue in many application fields, e.g., intrusion detection and grid computing. Detecting the changes in massive streaming data with a non-stationary distribution helps to alarm the anomalies, to clean the noises, and to report the new patterns. In this paper, we employ a novel approach for detecting changes in streaming data with the purpose of improving the quality of modeling the data streams. Through observing the outliers, this approach of change detection uses a weighted standard deviation to monitor the evolution of the distribution of data streams. A cumulative statistical test, Page-Hinkley, is employed to collect the evidence of changes in distribution. The parameter used for reporting the changes is self-adaptively adjusted according to the distribution of data streams, rather than set by a fixed empirical value. The self-adaptability of the novel approach enhances the effectiveness of modeling data streams by timely catching the changes of distributions. We validated the approach on an online clustering framework with a benchmark KDDcup 1999 intrusion detection data set as well as with a real-world grid data set. The validation results demonstrate its better performance on achieving higher accuracy and lower percentage of outliers comparing to the other change detection approaches.

Keywords

Change detection Data stream Self-adaptive parameter setting Non-stationary distribution 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Xiangliang Zhang
    • 1
  • Wei Wang
    • 2
  1. 1.Mathematical and Computer Sciences and Engineering DivisionKing Abdullah University of Science and TechnologySaudi Arabia
  2. 2.Interdisciplinary Centre for Security, Reliability and Trust (SnT Centre)University of LuxembourgLuxembourg

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