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A Distributed and Privacy-Preserving Method for Network Intrusion Detection

  • Fatiha Benali
  • Nadia Bennani
  • Gabriele Gianini
  • Stelvio Cimato
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6427)

Abstract

Organizations security becomes increasingly more difficult to obtain due to the fact that information technology and networking resources are dispersed across organizations. Network intrusion attacks are more and more difficult to detect even if the most sophisticated security tools are used. To address this problem, researchers and vendors have proposed alert correlation, an analysis process that takes the events produced by the monitoring components and produces compact reports on the security status of the organization under monitoring. Centralized solutions imply to gather from distributed resources by a third party the global state of the network in order to evaluate risks of attacks but neglect the honest but curious behaviors. In this paper, we focus on this issue and propose a set of solutions able to give a coarse or a fine grain global state depending on the system needs and on the privacy level requested by the involved organizations.

Keywords

Bayesian Network Information System Intrusion Detection Intrusion Detection System Trusted Third Party 
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 2010

Authors and Affiliations

  • Fatiha Benali
    • 1
  • Nadia Bennani
    • 2
  • Gabriele Gianini
    • 3
  • Stelvio Cimato
    • 3
  1. 1.CITI, INSA-LyonFrance
  2. 2.Université de Lyon, CNRS, INSA-Lyon, LIRIS, UMR5205France
  3. 3.Universitá degli Studi di MilanoMilanoItaly

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