Date of Award
8-22-2019
Document Type
Dissertation
Degree Name
Doctor of Philosophy (PhD)
Department
Department of Operational Sciences
First Advisor
Darryl K. Ahner, PhD
Abstract
Modern security threats are characterized by a stochastic, dynamic, partially observable, and ambiguous operational environment. This dissertation addresses such complex security threats using operations research techniques for decision making under uncertainty in operations planning, analysis, and assessment. First, this research develops a new method for robust queue inference with partially observable, stochastic arrival and departure times, motivated by cybersecurity and terrorism applications. In the dynamic setting, this work develops a new variant of Markov decision processes and an algorithm for robust information collection in dynamic, partially observable and ambiguous environments, with an application to a cybersecurity detection problem. In the adversarial setting, this work presents a new application of counterfactual regret minimization and robust optimization to a multi-domain cyber and air defense problem in a partially observable environment.
AFIT Designator
AFIT-ENS-DS-19-S-041
DTIC Accession Number
AD1084452
Recommended Citation
Keith, Andrew J., "Operational Decision Making under Uncertainty: Inferential, Sequential, and Adversarial Approaches" (2019). Theses and Dissertations. 2464.
https://scholar.afit.edu/etd/2464