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Improving Anomaly Detection for Text-Based Protocols by Exploiting Message Structures

Author

Listed:
  • Martin Güthle

    (Institute of Communication Networks and Computer Engineering (IKR), University of Stuttgart, Germany)

  • Jochen Kögel

    (Institute of Communication Networks and Computer Engineering (IKR), University of Stuttgart, Germany)

  • Stefan Wahl

    (Bell-Labs Germany, Alcatel-Lucent Deutschland AG, Stuttgart, Germany)

  • Matthias Kaschub

    (Institute of Communication Networks and Computer Engineering (IKR), University of Stuttgart, Germany)

  • Christian M. Mueller

    (Institute of Communication Networks and Computer Engineering (IKR), University of Stuttgart, Germany)

Abstract

Service platforms using text-based protocols need to be protected against attacks. Machine-learning algorithms with pattern matching can be used to detect even previously unknown attacks. In this paper, we present an extension to known Support Vector Machine (SVM) based anomaly detection algorithms for the Session Initiation Protocol (SIP). Our contribution is to extend the amount of different features used for classification (feature space) by exploiting the structure of SIP messages, which reduces the false positive rate. Additionally, we show how combining our approach with attribute reduction significantly improves throughput.

Suggested Citation

  • Martin Güthle & Jochen Kögel & Stefan Wahl & Matthias Kaschub & Christian M. Mueller, 2010. "Improving Anomaly Detection for Text-Based Protocols by Exploiting Message Structures," Future Internet, MDPI, vol. 2(4), pages 1-8, December.
    • Handle: RePEc:gam:jftint:v:2:y:2010:i:4:p:662-669:d:10655
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