Predicting Cybersecurity Incidents via Self-Reported Behavioral and Psychological Indicators: A Stratified Logistic Regression Approach

This study presents a novel and interpretable, deployment-ready framework for predicting cybersecurity incidents through item-level behavioral, cognitive, and dispositional indicators. Based on survey data from 453 professionals across countries and sectors, we developed 72 logistic regression models across twelve self-reported incident outcomes—from account lockouts to full device compromise—within six analytically stratified layers (Education, IT, Hungary, UK, USA, and full sample). Drawing on five theoretically grounded domains—cybersecurity behavior, digital literacy, personality traits, risk rationalization, and work–life boundary blurring—our models preserve the full granularity of individual responses rather than relying on aggregated scores, offering rare transparency and interpretability for real-world applications. This approach reveals how stratified models, despite smaller sample sizes, often outperform general ones by capturing behavioral and contextual specificity. Moderately prevalent outcomes (e.g., suspicious logins, multiple mild incidents) yielded the most robust predictions, while rare-event models, though occasionally high in “Area Under the Receiver Operating Characteristic Curve” (AUC), suffered from overfitting under cross-validation. Beyond model construction, we introduce threshold calibration and fairness-aware integration of demographic variables, enabling ethically grounded deployment in diverse organizational contexts. By unifying theoretical depth, item-level precision, multilayer stratification, and operational guidance, this study establishes a scalable blueprint for human-centric cybersecurity. It bridges the gap between behavioral science and risk analytics, offering the tools and insights needed to detect, predict, and mitigate user-level threats in increasingly blurred digital environments.