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Analyzing TCP Traffic Patterns Using Self Organizing Maps

  • Stefano Zanero
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3617)

Abstract

The continuous evolution of the attacks against computer networks has given renewed strength to research on anomaly based Intrusion Detection Systems, capable of automatically detecting anomalous deviations in the behavior of a computer system. While data mining and learning techniques have been successfully applied in host-based intrusion detection, network-based applications are more difficult, for a variety of reasons, the first being the curse of dimensionality. We have proposed a novel architecture which implements a network-based anomaly detection system using unsupervised learning algorithms. In this paper we describe how the pattern recognition features of a Self Organizing Map algorithm can be used for Intrusion Detection purposes on the payload of TCP network packets.

Keywords

Intrusion Detection Anomaly Detection Intrusion Detection System Network Intrusion Detection Packet Payload 
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 2005

Authors and Affiliations

  • Stefano Zanero
    • 1
  1. 1.D.E.I.-Politecnico di MilanoMilanoItaly

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