A User-Behavior Micro-Segmentation Framework for Cyber security in Work-From-Home Environments

<ns5:p>Background The rapid adoption of work-from-home (WFH) arrangements in higher education has increased dependence on personal devices and home networks, exposing universities to cybersecurity risks influenced by user behavior, usability challenges, and environmental constraints. This study aimed to develop a User-Behavior Micro-Segmentation Framework (UBMSF) tailored to the contextual and operational realities of remote work in Ugandan universities. Methods A quantitative descriptive research design grounded in the Theory of Planned Behavior (TPB) and Design Science Research (DSR) principles was employed. Data were collected using structured questionnaires administered to academic, administrative, and information technology staff from selected Ugandan universities. Descriptive statistics, correlation analysis, multiple regression, and k-means clustering were used to analyse cybersecurity behavior, usability difficulty, digital fatigue, and behavioral patterns. Results The findings revealed that perceived behavioral control, usability difficulty, and digital fatigue were significant predictors of cybersecurity behavior. Regression analysis showed that usability difficulty and fatigue negatively affected secure practices, while clustering analysis identified distinct user segments with varying levels of risk exposure, capability, and behavioral consistency. These findings informed the development of the UBMSF, which integrates behavioral, technological, and contextual dimensions to support adaptive cybersecurity interventions. Conclusions The study demonstrates that cybersecurity behavior in remote work environments is shaped by both psychological and contextual factors. The proposed framework provides a structured approach for identifying user behavior patterns and aligning cybersecurity interventions with user capability, usability conditions, and environmental realities in higher education institutions.</ns5:p>