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Pattern Analysis and Applications

, Volume 15, Issue 4, pp 459–475 | Cite as

Detecting unknown computer worm activity via support vector machines and active learning

  • Nir Nissim
  • Robert Moskovitch
  • Lior Rokach
  • Yuval Elovici
Industrial and Commercial Application

Abstract

To detect the presence of unknown worms, we propose a technique based on computer measurements extracted from the operating system. We designed a series of experiments to test the new technique by employing several computer configurations and background application activities. In the course of the experiments, 323 computer features were monitored. Four feature-ranking measures were used to reduce the number of features required for classification. We applied support vector machines to the resulting feature subsets. In addition, we used active learning as a selective sampling method to increase the performance of the classifier and improve its robustness in the presence of misleading instances in the data. Our results indicate a mean detection accuracy in excess of 90 %, and an accuracy above 94 % for specific unknown worms using just 20 features, while maintaining a low false-positive rate when the active learning approach is applied.

Keywords

Malware detection Supervised learning Active learning 

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Copyright information

© Springer-Verlag London Limited 2012

Authors and Affiliations

  • Nir Nissim
    • 1
    • 2
  • Robert Moskovitch
    • 1
    • 2
  • Lior Rokach
    • 1
    • 2
    • 2
  • Yuval Elovici
    • 1
    • 2
  1. 1.Department of Information Systems EngineeringBen Gurion University of the NegevBeer-ShevaIsrael
  2. 2.Deutsche Telekom LaboratoriesBen Gurion UniversityBeer-ShevaIsrael

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