On the Effectiveness of Different Botnet Detection Approaches

  • Fariba Haddadi
  • Duc Le Cong
  • Laura Porter
  • A. Nur Zincir-Heywood
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9065)


Botnets represent one of the most significant threats against cyber security. They employ different techniques, topologies and communication protocols in different stages of their lifecycle. Hence, identifying botnets have become very challenging specifically given that they can upgrade their methodology at any time. In this work, we investigate four different botnet detection approaches based on the technique used and type of data employed. Two of them are public rule based systems (BotHunter and Snort) and the other two are data mining based techniques with different feature extraction methods (packet payload based and traffic flow based). The performance of these systems range from 0% to 100% on the five publicly available botnet data sets employed in this work. We discuss the evaluation results for these different systems, their features and the models learned by the data mining based techniques.


Feature extraction traffic analysis botnet detection 


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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Fariba Haddadi
    • 1
  • Duc Le Cong
    • 1
  • Laura Porter
    • 1
  • A. Nur Zincir-Heywood
    • 1
  1. 1.Faculty of Computer ScienceDalhousie UniversityHalifaxCanada

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