Hilger, Ryan, authorSimske, Steve, advisorCross, Jennifer, committee memberDaily, Jeremy, committee memberRay, Indrakshi, committee member2025-09-012025-09-012025https://hdl.handle.net/10217/241844https://doi.org/10.25675/3.02164This dissertation bridges critical gaps between cybersecurity frameworks and interdisciplinary resilience theory through innovative algorithmic analysis. Rather than pursuing an elusive singular definition of resilience, I employ statistical modeling and machine learning techniques to extract core resilience attributes from a diverse corpus of 102 unique definitions across fields including ecology, psychology, disaster management, and organizational studies. My research addresses two fundamental questions: (1) Does any existing cybersecurity strategy or guidance document comprehensively address resilience across temporal and scalar dimensions? (2) How do current frameworks conceptualize and operationalize resilience? The methodological approach integrates term frequency-inverse document frequency (tf*idf), Latent Dirichlet Allocation, and bidirectional encoder representations from transformers (BERT) algorithms to construct a novel classification scaffold based on time and scale dimensions. This scaffold systematically evaluates 37 cybersecurity frameworks and 12 non-cyber resilience frameworks against core resilience attributes. Results reveal significant gaps between cybersecurity guidance and interdisciplinary resilience concepts, with most frameworks focusing predominantly on technical and sociotechnical aspects while neglecting broader organizational, community, and temporal dimensions of resilience. This research makes several key contributions: (1) establishing a data-driven classification framework for assessing resilience features in guidance documents, (2) demonstrating that no single existing framework adequately addresses resilience across all relevant dimensions, and (3) providing a foundation for developing more comprehensive cyber resilience strategies. The findings offer both theoretical advancement in conceptualizing cyber resilience and practical guidance for organizations seeking to build more adaptable and resilient systems across multiple time horizons and organizational scales.born digitaldoctoral dissertationsengCopyright and other restrictions may apply. User is responsible for compliance with all applicable laws. For information about copyright law, please see https://libguides.colostate.edu/copyright.machine learningstatistical modelingresiliencecybersecurityText