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<doi>0290-cd</doi>
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<article-title>An Approach to Software Assisted Physical Security Risk Analysis and Optimization</article-title>
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<author>Daniel Lichte<sup>a</sup>, Dustin Witte<sup>b</sup> and Kai-Dietrich Wolf<sup>c</sup></author>

<aff>Institute for Security Systems, University of Wuppertal, Germany</aff>
<email><a href="mailto:lichte@iss.uni-wuppertal.de"><sup>a</sup>lichte@iss.uni-wuppertal.de</a></email>
<email><a href="mailto:dustin.witte@uni-wuppertal.de"><sup>b</sup>dustin.witte@uni-wuppertal.de</a></email>
<email><a href="mailto:wolf@iss.uni-wuppertal.de"><sup>c</sup>wolf@iss.uni-wuppertal.de</a></email>


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<title>ABSTRACT</title>
<p>Increasing awareness for terrorist activity has brought the security of critical infrastructures into the focus of operators as well as politics. The result is a growing demand for security risk analysis and optimization methods to ensure the physical and IT-security of critical infrastructures. Practical experience shows that both fields are mostly covered by qualitative methods. In contrast, quantitative methods and models are rarely used, although various approaches have been developed. Among the main reasons for this limited application are the complexity of implementation and the availability of quantitative data despite the fact that quantitative results allow for further analysis and also have additional benefits. In order to facilitate quantitative security risk analysis in practice, this paper introduces a software assisted method which simplifies analysis by GUI-based modeling, evaluation and optimization of physical security systems of infrastructures. The evaluation enables both, analysis of existing security systems and comparison of configurations using a risk-based performance indicator, the Return on Security Investment (ROSI). The underlying cost functions of security measures are derived by considering life cycle costs. Additionally, the introduction of the ROSI enables a cost-benefit optimization of physical security in infrastructures considering budgetary constraints using a differential evolution algorithm. The capabilities of the software assisted method are demonstrated considering a fictitious airport infrastructure.</p>
<p><italic>Keywords: </italic>Physical Security, Critical Infrastructure Protection, Quantitative Methods, Security Risk Analysis, Security Optimization, Cost-Benefit Optimization, ROSI, GUI-based Modeling, Software Framework</p>
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