A Systematic Review of Differential Equation Models of Insider Threat Dynamics: Methods, Architectures, and Future Research Directions

Abstract

Insider threats constitute one of the most complex and damaging challenges in cybersecurity, arising from authorized individuals who misuse legitimate access to compromise organizational systems. Unlike external attacks, they are difficult to detect due to behavioral variability, contextual ambiguity, and seemingly normal access patterns. Traditional rule-based and static anomaly detection methods often fail to capture the dynamic and time-dependent nature of insider behavior. This review examines the application of differential equation-based models and dynamic system approaches in modeling insider threats, synthesizing insights from recent peer-reviewed studies. It highlights a transition from purely data-driven machine learning techniques to hybrid models that integrate dynamical systems, stochastic differential equations, and time-series analysis for capturing continuous behavioral evolution. Key methodologies include agent-based simulations, game-theoretic models, neural ordinary differential equations, and hybrid deep learning–differential frameworks, which enhance the modeling of causal relationships, feedback loops, and long-term behavioral trends. Despite these advancements, challenges persist in scalability, computational complexity, and limited real-world validation. The review identifies a critical gap in combining explainable mathematical models with adaptive AI systems for real-time deployment, suggesting future directions such as neuro-dynamic hybrid systems, physics-informed modeling, and scalable architectures to improve detection accuracy and interpretability.