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Quantitative Information Security Risk Estimation Using Probabilistic Attack Graphs

  • Pontus Johnson
  • Alexandre VernotteEmail author
  • Dan Gorton
  • Mathias Ekstedt
  • Robert Lagerström
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10224)

Abstract

This paper proposes an approach, called pwnPr3d, for quantitatively estimating information security risk in ICT systems. Unlike many other risk analysis approaches that rely heavily on manual work and security expertise, this approach comes with built-in security risk analysis capabilities. pwnPr3d combines a network architecture modeling language and a probabilistic inference engine to automatically generate an attack graph, making it possible to identify threats along with the likelihood of these threats exploiting a vulnerability. After defining the value of information assets to their organization with regards to confidentiality, integrity and availability breaches, pwnPr3d allows users to automatically quantify information security risk over time, depending on the possible progression of the attacker. As a result, pwnPr3d provides stakeholders in organizations with a holistic approach that both allows high-level overview and technical details.

Keywords

Quantitative risk analysis Attack graphs Threat modeling Network security Information security 

Notes

Acknowledgments

The work presented in this paper has received funding from the European Unions Seventh Framework Programme for research, technological development and demonstration under grant agreement no. 607109 as well as the Swedish Civil Contingencies Agency (MSB) through the research centre on Resilient Information and Control Systems (RICS).

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Pontus Johnson
    • 1
  • Alexandre Vernotte
    • 1
    Email author
  • Dan Gorton
    • 2
  • Mathias Ekstedt
    • 1
  • Robert Lagerström
    • 1
  1. 1.KTH Royal Institute of TechnologyStockholmSweden
  2. 2.Foreseeti ABStockholmSweden

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