Publications

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Of the many facets of the criminal world, few have captured societys fascination as has that of high stakes robbery. The combination of meticulousness, cunning, and audacity required to execute a real-life Oceans Eleven may be uncommon among criminals, but fortunately it is common enough to extract a wealth of lessons for the protection of high-value assets. To assist in informing the analyses and decisions of security professionals, this paper surveys 23 sophisticated and high-value heists that have occurred or been attempted around the world, particularly over the past three decades. The results, compiled in a Heist Methods and Characteristics Database, have been analyzed qualitatively and quantitatively, with the goals of both identifying common characteristics and characterizing the range and diversity of criminal methods used. The analysis is focused in six areas: (1) Defeated Security Measures and Devices, (2) Deception Methods, (3) Timing, (4) Weapons, (5) Resources, and (6) Insiders.

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This paper presents Sandia National Laboratories' Out-door Weapons of Mass Destruction Decision Analysis Center (Out-DAC) and, through an example case study, derives lessons for its use. This tool, related to similar capabilities at Sandia, can be used to determine functional requirements for a detection system of aerosol-released threats outdoors. Essential components of OutDAC are a population database, a meteorological dataset, an atmospheric transport and dispersion model and an optimization toolkit. Detector placement is done through optimization against a library of hypothe-sized attack scenarios by minimizing either the mean or value-at-risk of undetected infections. These scenarios are the product of a Monte Carlo simulation intended to characterize the uncertainty associated with the threat. An example case study illustrates that Monte Carlo convergence is dependent on the statistic of interest. Furthermore, the quality of the detector placement optimization may be tied to the convergence level of the Monte Carlo simulation. © 2009 IEEE.

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