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Detecting Broad Length Algorithmically Generated Domains

  • Aashna Ahluwalia
  • Issa TraoreEmail author
  • Karim Ganame
  • Nainesh Agarwal
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10618)

Abstract

Domain generation algorithm (DGA) represents a safe haven for modern botnets, as it enables them to escape detection. Due to the fact that DGA domains are generated randomly, they tend to be unusually long, which can be leveraged toward detecting them. Shorter DGA domains, in contrast, are more difficult to detect, as most legitimate domains are relatively short. We introduce in this paper, a new detection model that uses information theoretic features, and leverage the notion of domain length threshold to detect dynamically and transparently DGA domains regardless of their lengths. Experimental evaluation of the approach using public datasets yields detection rate (DR) of 98.96% and false positive rate (FPR) of 2.1%, when using random forests classification technique.

Keywords

HTTP botnet Botnet detection Machine learning Passive DNS DGA domains Malicious fast flux DNS Domain length 

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Aashna Ahluwalia
    • 1
  • Issa Traore
    • 1
    Email author
  • Karim Ganame
    • 2
  • Nainesh Agarwal
    • 3
  1. 1.ECE DepartmentUniversity of VictoriaVictoriaCanada
  2. 2.StreamScanMontrealCanada
  3. 3.BC Provincial GovernmentVictoriaCanada

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