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OPEM: A Static-Dynamic Approach for Machine-Learning-Based Malware Detection

  • Igor Santos
  • Jaime Devesa
  • Félix Brezo
  • Javier Nieves
  • Pablo Garcia Bringas
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 189)

Abstract

Malware is any computer software potentially harmful to both computers and networks. The amount of malware is growing every year and poses a serious global security threat. Signature-based detection is the most extended method in commercial antivirus software, however, it consistently fails to detect new malware. Supervised machine learning has been adopted to solve this issue. There are two types of features that supervised malware detectors use: (i) static features and (ii) dynamic features. Static features are extracted without executing the sample whereas dynamic ones requires an execution. Both approaches have their advantages and disadvantages. In this paper, we propose for the first time, OPEM, an hybrid unknown malware detector which combines the frequency of occurrence of operational codes (statically obtained) with the information of the execution trace of an executable (dynamically obtained). We show that this hybrid approach enhances the performance of both approaches when run separately.

Keywords

malware hybrid static dynamic machine learning computer security 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Igor Santos
    • 1
  • Jaime Devesa
    • 1
  • Félix Brezo
    • 1
  • Javier Nieves
    • 1
  • Pablo Garcia Bringas
    • 1
  1. 1.S3Lab, DeustoTech - ComputingDeusto Institute of Technology University of DeustoBilbaoSpain

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