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A SVM-Based Behavior Monitoring Algorithm towards Detection of Un-desired Events in Critical Infrastructures

  • Y. Jiang
  • J. Jiang
  • P. Capodieci
Conference paper
Part of the Advances in Intelligent and Soft Computing book series (AINSC, volume 63)

Abstract

In this paper, we report our recent research activities under MICIE, a European project funded under Framework-7 Programme, in which a SVM-based behavior modeling and learning algorithm is described. The proposed algorithm further exploits the adapted learning capability in SVM by using statistics analysis and K-S test verification to introduce an automated parameter control mechanism, and hence the SVM learning and detection can be made adaptive to the statistics of the input data. Experiments on telecommunication network data sets support that the proposed algorithm is able to detect undesired events effectively, presenting a good potential for development of computer-aided monitoring software tools for protection of critical infrastructures.

Keywords

Outlier Detection Anomaly Detection Critical Infrastructure Information Communication Technology Network Traffic Data 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Y. Jiang
    • 1
  • J. Jiang
    • 1
  • P. Capodieci
    • 2
  1. 1.Digital Media & Systems Research InstituteUniversity of BradfordUK
  2. 2.Selex Communications S.p.AItaly

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