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Ensemble of Feature Chains for Anomaly Detection

  • Lena Tenenboim-Chekina
  • Lior Rokach
  • Bracha Shapira
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7872)

Abstract

Along with recent technological advances more and more new threats and advanced cyber-attacks appear unexpectedly. Developing methods which allow for identification and defense against such unknown threats is of great importance. In this paper we propose new ensemble method (which improves over the known cross-feature analysis, CFA, technique) allowing solving anomaly detection problem in semi-supervised settings using well established supervised learning algorithms. Theoretical correctness of the proposed method is demonstrated. Empirical evaluation results on Android malware datasets demonstrate effectiveness of the proposed approach and its superiority against the original CFA detection method.

Keywords

ensemble methods machine learning anomaly detection probabilistic methods network monitoring Android malware 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Lena Tenenboim-Chekina
    • 1
  • Lior Rokach
    • 1
  • Bracha Shapira
    • 1
  1. 1.Department of Information Systems Eng. and Telekom Innovation LaboratoriesBen-Gurion University of the NegevIsrael

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