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Detection of Anomalies in a SOA System by Learning Algorithms

  • Ilona Bluemke
  • Marcin Tarka
Conference paper
Part of the Advances in Intelligent and Soft Computing book series (AINSC, volume 170)

Abstract

The objective of this chapter is to present the detection of anomalies in SOA system by learning algorithms. As it was not possible to inject errors into the “real” SOA system and to measure them, a special model of SOA system was designed and implemented. In this systems several anomalies were introduced and the effectiveness of algorithms in detecting them were measured. The results of experiments may be used to select efficient algorithm for anomaly detection. Two algorithms: K-Means clustering and emerging patterns were used to detect anomalies in the frequency of service call. The results of this experiment are discussed.

Keywords

False Alarm Intrusion Detection Anomaly Detection Service Oriented Architecture Service Call 
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 2013

Authors and Affiliations

  1. 1.Institute of Computer ScienceWarsaw University of TechnologyWarsawPoland

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