Dissertation/Thesis Abstract

Allocation of Resources to Defend Spatially Distributed Networks Using Game Theoretic Allocations
by Kroshl, William M., Ph.D., The George Washington University, 2015, 146; 3669710
Abstract (Summary)

This dissertation presents research that focuses on efficient allocation of defense resources to minimize the damage inflicted on a spatially distributed physical network such as a pipeline, water system, or power distribution system from an attack by an active adversary. The allocation methodology recognizes the fundamental difference between preparing for natural disasters such as hurricanes, earthquakes, or even accidental systems failures and the problem of allocating resources to defend against an opponent who is aware of and anticipating the defender's efforts to mitigate the threat.

Conceptualizing the problem as a Stackelberg "leader-follower" game, the defender first places his assets to defend key areas of the network, and the attacker then seeks to inflict the maximum damage possible within the constraints of resources and network structure. The approach is to utilize a combination of integer programming and agent-based modeling to allocate the defensive resources. The criticality of arcs in the network is estimated by a deterministic network interdiction formulation, a maximum-flow linear program (LP), or a combination of both of these methods, which then inform an evolutionary agent-based simulation. The evolutionary agent-based simulation is used to determine the allocation of resources for attackers and defenders that results in evolutionarily stable strategies in which actions by either side alone cannot increase their share of victories.

These techniques are demonstrated on several example networks using several different methods of evaluating the value of the nodes and comparing the evolutionary agent-based results to a more traditional, Probabilistic Risk Analysis (PRA) approach. The results show that the agent-based allocation approach results in a greater percentage of defender victories than does the PRA-based allocation approach.

Indexing (document details)
Advisor: Mazzuchi, Thomas, Sarkani, Shahram
Commitee: Dever, Jason, Fomin, Pavel, Mazzuchi, Thomas, Murphree, E. Lile, Sarkani, Shahram
School: The George Washington University
Department: Engineering Mgt and Systems Engineering
School Location: United States -- District of Columbia
Source: DAI-B 76/05(E), Dissertation Abstracts International
Source Type: DISSERTATION
Subjects: Systems science, Operations research
Keywords: Agent based simulation, Game theory, Networks, Risk analysis
Publication Number: 3669710
ISBN: 978-1-321-45526-7
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