USING NETWORK MOTIFS TO SEARCH FOR INDICATORS OF MALIGN FOREIGN ACTIVITIES IN SOCIOTECHNICAL NETWORK MODELS
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Authors
Shannon, Ryan A.
Subjects
socio-technical network analysis
critical infrastructure
CI
complex systems
graph-based anomaly detection
GBAD
structural motif
counter-foreign influence
critical infrastructure
CI
complex systems
graph-based anomaly detection
GBAD
structural motif
counter-foreign influence
Advisors
Eisenberg, Daniel
Date of Issue
2024-09
Date
Publisher
Monterey, CA; Naval Postgraduate School
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Abstract
The vast majority of critical infrastructure (CI) research studies vulnerabilities to system disruption via disaster or attack. However, few studies assess the social networks and businesses that own and operate CI for potential adversarial influence and fraud. For example, if an adversary wishes to access a well-defended system, they could forgo an attack and use legitimate business practices such as mergers, hostile takeovers, and foreign investment to gain access. This work aims to develop analytical techniques that can identify these vulnerabilities. We access and combine three different public data sources to create network models of the people and organizations surrounding CI. Then, we develop a technique using motifs to search the networks for common structural markers that a near-peer adversary uses to influence businesses. We apply our technique to networks generated for the electric vehicle charging industry. Our results indicate that motifs are a simple and effective way to search large networks for indications of malign influence. This work serves as a proof-of-concept that will enable the creation of automated investigative tools to identify and deter foreign and malign influence in emerging CI systems.
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Thesis
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Sponsors
Department of Energy, Idaho Operations Office, 1955 Fremont Ave, Idaho Falls, ID 83402
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Distribution Statement
Distribution Statement A. 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.