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Stochastic Bayesian Games for the Cybersecurity of Nuclear Power Plants

Maccarone, Lee (2021) Stochastic Bayesian Games for the Cybersecurity of Nuclear Power Plants. Doctoral Dissertation, University of Pittsburgh. (Unpublished)

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The goal of this research is to reduce the likelihood of successful attacks on nuclear power plants. Cyber-physical systems such as nuclear power plants consist of interconnected physical processes and computational resources. Because the cyber and physical worlds are integrated, vulnerabilities in both the cyber and physical domains can result in physical damage to the system. Nuclear power plants can be targeted by a variety of adversaries — each with a unique motivation and set of resources. To secure nuclear power plants and other cyber-physical systems, we require an approach to security that also accounts for the interactions of human decision-makers.

This research uses a game-theoretic approach to nuclear cybersecurity. The cybersecurity of the plant can be viewed as a non-cooperative game between a defender and an attacker. The field of game theory provides a mathematical framework to analyze the interactions of the defender and attacker as both players seek to accomplish their objectives. In this research, a stochastic Bayesian game is used to optimize cybersecurity decision-making. A stochastic Bayesian game is a combination of a stochastic game and a Bayesian game. The stochastic elements of the game enable the consideration of uncertainty in the interactions of the attacker and defender. The Bayesian elements of the game enable the consideration of the uncertainty regarding the attacker's characteristics. This combination is useful for the analysis of nuclear power plant cybersecurity because it enables plant defenders to optimize their security decisions in the presence of uncertainty.


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Item Type: University of Pittsburgh ETD
Status: Unpublished
CreatorsEmailPitt UsernameORCID
Maccarone, Leeltm10@pitt.edultm100000-0002-2023-0255
ETD Committee:
TitleMemberEmail AddressPitt UsernameORCID
Committee ChairCole, Danieldgcole@pitt.edudgcole
Committee MemberClark, Williamwclark@pitt.eduwclark
Committee MemberBajaj, Nikhilnbajaj@pitt.edunbajaj
Committee MemberAbdelhakim, Maimaia@pitt.edumaia
Committee MemberLamb,
Date: 3 September 2021
Date Type: Publication
Defense Date: 3 June 2021
Approval Date: 3 September 2021
Submission Date: 10 July 2021
Access Restriction: No restriction; Release the ETD for access worldwide immediately.
Number of Pages: 199
Institution: University of Pittsburgh
Schools and Programs: Swanson School of Engineering > Mechanical Engineering
Degree: PhD - Doctor of Philosophy
Thesis Type: Doctoral Dissertation
Refereed: Yes
Uncontrolled Keywords: Game theory, cybersecurity, artificial intelligence, Bayesian learning, nuclear power, critical infrastructure
Date Deposited: 03 Sep 2021 16:25
Last Modified: 03 Sep 2021 16:25


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