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Intelligent Adversary Risk Analysis: A Bioterrorism Risk Management Model

Authors

  • Gregory S. Parnell,

    Corresponding author
      *Address correspondence to Gregory S. Parnell, Department of Systems Engineering, United States Military Academy at West Point, USA; gregory.parnell@usma.edu.
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    • 1

      Department of Systems Engineering, United States Military Academy at West Point, NY, USA and Innovative Decisions Inc., Vienna, VA, USA.

  • Christopher M. Smith,

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    • 2

      Department of Mathematical Sciences, United States Military Academy at West Point, NY, USA.

  • Frederick I. Moxley

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    • 3

      Department of Electrical Engineering and Computer Science, United States Military Academy at West Point, NY, USA.


*Address correspondence to Gregory S. Parnell, Department of Systems Engineering, United States Military Academy at West Point, USA; gregory.parnell@usma.edu.

Abstract

The tragic events of 9/11 and the concerns about the potential for a terrorist or hostile state attack with weapons of mass destruction have led to an increased emphasis on risk analysis for homeland security. Uncertain hazards (natural and engineering) have been successfully analyzed using probabilistic risk analysis (PRA). Unlike uncertain hazards, terrorists and hostile states are intelligent adversaries who can observe our vulnerabilities and dynamically adapt their plans and actions to achieve their objectives. This article compares uncertain hazard risk analysis with intelligent adversary risk analysis, describes the intelligent adversary risk analysis challenges, and presents a probabilistic defender–attacker–defender model to evaluate the baseline risk and the potential risk reduction provided by defender investments. The model includes defender decisions prior to an attack; attacker decisions during the attack; defender actions after an attack; and the uncertainties of attack implementation, detection, and consequences. The risk management model is demonstrated with an illustrative bioterrorism problem with notional data.

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