Algorithms for efficient intelligence collection
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Authors
Duncan, Ellis, R.
Subjects
Analysis of algorithms, intelligence collection, graphical models, Bayesian inference, Markov random fields, networks
Advisors
Dimitrov, Nedialko B.
Date of Issue
2013-09
Date
Sep-13
Publisher
Monterey, California: Naval Postgraduate School
Language
Abstract
Modern intelligence techniques have drastically increased the rate at which communications data can be intercepted. The increased ability to collect and store this data poses a significant processing problem for intelligence agencies. We develop a software library, implementing a previously developed mathematical model of the information selection problem facing these agencies: given a time constraint, which items should be screened in order to maximize the relevant information obtained. Using our software, we analyze the performance of several screening strategies on a variety of representative intercepted intelligence networks, which we construct using real world data sets. We show the model consistently outperforms more naive approaches on networks with clusters of relevant sources, and highlight the importance of exploration in robust screening strategies.
Type
Thesis
Description
Series/Report No
Department
Operations Research
Organization
Identifiers
NPS Report Number
Sponsors
Funding
Format
Citation
Distribution Statement
Approved for public release; distribution is unlimited.
Rights
This publication is a work of the U.S. Government as defined in Title 17, United States Code, Section 101. Copyright protection is not available for this work in the United States.