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Automatically Generating Models for Botnet Detection

  • Peter Wurzinger
  • Leyla Bilge
  • Thorsten Holz
  • Jan Goebel
  • Christopher Kruegel
  • Engin Kirda
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5789)

Abstract

A botnet is a network of compromised hosts that is under the control of a single, malicious entity, often called the botmaster. We present a system that aims to detect bots, independent of any prior information about the command and control channels or propagation vectors, and without requiring multiple infections for correlation. Our system relies on detection models that target the characteristic fact that every bot receives commands from the botmaster to which it responds in a specific way. These detection models are generated automatically from network traffic traces recorded from actual bot instances. We have implemented the proposed approach and demonstrate that it can extract effective detection models for a variety of different bot families. These models are precise in describing the activity of bots and raise very few false positives.

Keywords

Intrusion Detection System Detection Model Behavior Cluster Token Sequence Network Trace 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Peter Wurzinger
    • 1
  • Leyla Bilge
    • 2
  • Thorsten Holz
    • 1
    • 3
  • Jan Goebel
    • 3
  • Christopher Kruegel
    • 4
  • Engin Kirda
    • 2
  1. 1.Secure Systems LabVienna University of TechnologyAustria
  2. 2.Institute EurecomSophia Antipolis
  3. 3.University of MannheimGermany
  4. 4.University of CaliforniaSanta Barbara

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