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Using Hidden Markov Models to Evaluate the Risks of Intrusions

System Architecture and Model Validation
  • André Årnes
  • Fredrik Valeur
  • Giovanni Vigna
  • Richard A. Kemmerer
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4219)

Abstract

Security-oriented risk assessment tools are used to determine the impact of certain events on the security status of a network. Most existing approaches are generally limited to manual risk evaluations that are not suitable for real-time use. In this paper, we introduce an approach to network risk assessment that is novel in a number of ways. First of all, the risk level of a network is determined as the composition of the risks of individual hosts, providing a more precise, fine-grained model. Second, we use Hidden Markov models to represent the likelihood of transitions between security states. Third, we tightly integrate our risk assessment tool with an existing framework for distributed, large-scale intrusion detection, and we apply the results of the risk assessment to prioritize the alerts produced by the intrusion detection sensors. We also evaluate our approach on both simulated and real-world data.

Keywords

Risk assessment Intrusion detection Hidden Markov modeling 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • André Årnes
    • 1
  • Fredrik Valeur
    • 2
  • Giovanni Vigna
    • 2
  • Richard A. Kemmerer
    • 2
  1. 1.Centre for Quantifiable Quality of Service in Communication SystemsNorwegian University of Science and TechnologyTrondheimNorway
  2. 2.Department of Computer ScienceUniversity of California Santa BarbaraSanta BarbaraUSA

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