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Accession Number:
AD1084452
Title:
Operational Decision Making Under Uncertainty: Inferential, Sequential, and Adversarial Approaches
Report Date:
2019-09-01
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.
Document Type:
Conference:
Journal:
Pages:
292
File Size:
4.30MB
Contracts:
Grants:
Distribution Statement:
Approved For Public Release
