Methodology of risk assessment in case of unauthorized actions of an internal violator

Abstract

An analytical framework is presented to assess and predict insider threats at nuclear installations by integrating behavioral indicators, access anomaly detection, rule based checks, and machine learning models. CERT dataset logs and psychometrics are fused into engineered features; ensemble and deep models produce interpretable composite risk scores for alerting and security response