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A Self-learning System for Detection of Anomalous SIP Messages

  • Konrad Rieck
  • Stefan Wahl
  • Pavel Laskov
  • Peter Domschitz
  • Klaus-Robert Müller
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5310)

Abstract

Current Voice-over-IP infrastructures lack defenses against unexpected network threats, such as zero-day exploits and computer worms. The possibility of such threats originates from the ongoing convergence of telecommunication and IP network infrastructures. As a countermeasure, we propose a self-learning system for detection of unknown and novel attacks in the Session Initiation Protocol (SIP). The system identifies anomalous content by embedding SIP messages to a feature space and determining deviation from a model of normality. The system adapts to network changes by automatically retraining itself while being hardened against targeted manipulations. Experiments conducted with realistic SIP traffic demonstrate the high detection performance of the proposed system at low false-positive rates.

Keywords

Intrusion Detection Session Initiation Protocol Intrusion Detection System Network Intrusion Detection Session Initiation Protocol Message 
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 2008

Authors and Affiliations

  • Konrad Rieck
    • 1
  • Stefan Wahl
    • 2
  • Pavel Laskov
    • 1
    • 3
  • Peter Domschitz
    • 2
  • Klaus-Robert Müller
    • 1
    • 4
  1. 1.Fraunhofer Institute FIRSTIntelligent Data AnalysisBerlinGermany
  2. 2.Bell Labs GermanyAlcatel-LucentStuttgartGermany
  3. 3.University of Tübingen, Wilhelm-Schickard-InstituteGermany
  4. 4.Dept. of Computer ScienceTechnical University of BerlinGermany

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