A Stochastic Programming Approach to the Design Optimization of Layered Physical Protection Systems

Brown, Nathanael J. K.; Jones, Katherine A.; Bandlow, Alisa; Waddell, Lucas; Nozick, Linda K.

doi:10.24251/hicss.2017.180

Cited by 1 publication

(1 citation statement)

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“…Authors use greedy algorithm to seed the genetic algorithm. This model is extended with support for connecting between weather, visibility conditions and intruder capabilities with detection probability, and interruption probability is calculated including the impact of nuisance and false alarm rates on operator performance [21]. Some of the listed articles expect predefined budget or optimize only one starting point or optimize predefined critical paths, without taking into consideration that applying security equipment changes critical paths.…”

Section: Introductionmentioning

confidence: 99%

Optimizing physical protection system using domain experienced exploration method

et al. 2019

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Assessing physical protection system efficiency is mostly done manually by security experts due to the complexity of the assessment process and lack of tools. Computer aided numerical vulnerability analysis has been developed to quantitatively assess physical protection systems. A variety of methods have been proposed to optimize physical protection systems, where one of the most advanced approaches entails precisely defining which security components should be selected and where they should be placed at protected facilities, taking into consideration adversary type, to maximize the probability that an adversary will be stopped at minimum system cost. The most computationally intensive part of the optimization process is the evaluation. The evaluation involves recreating search space and finding optimal adversary's attack paths from each entry point. We present the domain experienced exploration method that optimizes evaluation process during the search for optimum solutions, considering results from previous evaluations. Performed experiments show that using the presented method, in real-world domains, results in a reduction of evaluation iterations.

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Section: Introductionmentioning

confidence: 99%