A Graph-Based Mathematical Modeling of Digital Identity for Anomaly Detection and Future Attack Prediction

This study proposes a graph-based framework for anomaly detection and future attack prediction by redefining Digital Identity (DI) as a dynamic and structural security entity rather than a simple authentication credential. The proposed model integrates user, device, network, session, and behavioral information to construct a unique digital fingerprint and represents the relationships among DI components as a graph structure. Normal DI maintains consistent structural patterns, whereas attack situations introduce changes in relational structures and behavioral transition patterns, which are utilized as key indicators for anomaly detection. In addition, temporal correlation analysis is incorporated to extend the framework toward proactive prediction of potential future attacks. Experimental evaluations are conducted using public cybersecurity datasets and DI-based synthetic attack scenarios, demonstrating that the proposed framework achieves higher detection accuracy and improved structural interpretability compared with conventional anomaly detection methods. This study is meaningful in that it reinterprets DI as an active security entity capable of continuous integrity verification, anomaly detection, and future attack prediction.