Applying Machine Learning Techniques for Detection of Malicious Code in Network Traffic

  • Yuval Elovici
  • Asaf Shabtai
  • Robert Moskovitch
  • Gil Tahan
  • Chanan Glezer
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4667)

Abstract

The Early Detection, Alert and Response (eDare) system is aimed at purifying Web traffic propagating via the premises of Network Service Providers (NSP) from malicious code. To achieve this goal, the system employs powerful network traffic scanners capable of cleaning traffic from known malicious code. The remaining traffic is monitored and Machine Learning (ML) algorithms are invoked in an attempt to pinpoint unknown malicious code exhibiting suspicious morphological patterns. Decision trees, Neural Networks and Bayesian Networks are used for static code analysis in order to determine whether a suspicious executable file actually inhabits malicious code. These algorithms are being evaluated and preliminary results are encouraging.

Keywords

Malicious Code Machine Learning Network Service Provider (NSP) Feature Selection 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Yuval Elovici
    • 1
  • Asaf Shabtai
    • 1
  • Robert Moskovitch
    • 1
  • Gil Tahan
    • 1
  • Chanan Glezer
    • 1
  1. 1.Deutsche Telekom Laboratories at Ben-Gurion University, Be’er Sheva, 84105Israel

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