Uncertainty in interdependent security games

  • Authors:
  • Benjamin Johnson;Jens Grossklags;Nicolas Christin;John Chuang

  • Affiliations:
  • CyLab, Carnegie Mellon University;Center for Information Technology Policy, Princeton University;CyLab, Carnegie Mellon University;School of Information, UC Berkeley

  • Venue:
  • GameSec'10 Proceedings of the First international conference on Decision and game theory for security
  • Year:
  • 2010

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Abstract

Even the most well-motivated models of information security have application limitations due to the inherent uncertainties involving risk. This paper exemplifies a formal mechanism for resolving this kind of uncertainty in interdependent security (IDS) scenarios. We focus on a single IDS model involving a computer network, and adapt the model to capture a notion that players have only a very rough idea of security threats and underlying structural ramifications. We formally resolve uncertainty by means of a probability distribution on risk parameters that is common knowledge to all players. To illustrate how this approach might yield fruitful applications, we postulate a well-motivated distribution, compute Bayesian Nash equilibria and tipping conditions for the derived model, and compare these with the analogous conditions for the original IDS model.