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Statistical and Fuzzy Set Modeling for the Risk Analysis for Critical Infrastructure Protection

Cotellesso, Paul
http://rave.ohiolink.edu/etdc/view?acc_num=osu1250427229

Abstract Details

2009, Doctor of Philosophy, Ohio State University, Civil Engineering.
A survey of experts that work in the protection of critical infrastructure was developed and administered that captured the data upon which the analysis rests. The survey consisted of four major sections: consent, primary data acquisition, demographic data acquisition, and closing remarks. Primary data acquisition focused on four areas: weapon implementation likelihood and a three variable: consequence, threat, and vulnerability; risk function. There were two scales used in the primary data acquisition a forced rank order and a 7-point Likert scale.Non-parametric and parametric statistical models were used to analyze the data, describe the behavior, establish relationships, and explain the phenomena. The non-parametric model, Friedman’s test, was used to ascertain a rank order based on the data from weapon implementation likelihood in the four major weapon categories: conventional, chemical biological and nuclear. The parametric model consisted of the proportional odds model which was used to explore the relationships through the risk function. The proportional odds model calculated cumulative and individual probabilities of a response level, and point estimates of severity. Confidence intervals were projected using the bootstrap method and the models were validated using internal cross validation procedures. The proportional odds model estimates consequence, threat, and vulnerability values. These values can be used separately in risk management and analysis or manipulated using other methodologies. A fuzzy logic triangle model was used to estimate risk from the consequence, threat, and vulnerability values. The model represents a reasonable estimate of risk. Critical infrastructure protection is crucial to the functioning of US society, the economy, democracy, and national security. The terrorist attacks on the World Trade Center caused a shift in strategic, operational, and tactical policy toward improving critical infrastructure protection by the public and private sectors. The policies placed new emphasis on establishing safe, reliable, survivable, and resilient infrastructure. The magnitude of critical infrastructure and limited quantity of resources available to protect it necessitates establishing a scientific methodology upon which to develop policy and make decisions. This study advances the protection of critical infrastructure knowledge through the exploration of tactics, perimeter security functions, countermeasures, and buildings relationships.
Fabian Tab, PhD (Advisor)
William Wolfe, PhD (Committee Member)
Christopher Holloman, PhD (Committee Member)
443 p.
Civil Engineering
Critical Infrastructure Protection; Perimeter Security; Fuzzy Triangular Model; Proportional Odds Model; Friedman's Test

Recommended Citations

Citations

  • Cotellesso, P. (2009). Statistical and Fuzzy Set Modeling for the Risk Analysis for Critical Infrastructure Protection [Doctoral dissertation, Ohio State University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=osu1250427229

    APA Style (7th edition)

  • Cotellesso, Paul. Statistical and Fuzzy Set Modeling for the Risk Analysis for Critical Infrastructure Protection. 2009. Ohio State University, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=osu1250427229.

    MLA Style (8th edition)

  • Cotellesso, Paul. "Statistical and Fuzzy Set Modeling for the Risk Analysis for Critical Infrastructure Protection." Doctoral dissertation, Ohio State University, 2009. http://rave.ohiolink.edu/etdc/view?acc_num=osu1250427229

    Chicago Manual of Style (17th edition)

osu1250427229
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© 2009, all rights reserved.
This open access ETD is published by The Ohio State University and OhioLINK.