Integrated Threat Intelligence Automation and Simulation Models for Next Generation Cyber Defense

The evolution of increasingly sophisticated and persistent cyber threats has exposed the limitations of traditional, largely reactive defense mechanisms, especially in complex enterprise and critical infrastructure environments. This paper proposes an integrated framework that combines automated threat intelligence processing with advanced simulation models to enable predictive, proactive cyber defense. The framework automates the collection, normalization, and correlation of threat data from diverse sources such as OSINT, internal telemetry, and industry sharing platforms, feeding these into a simulation engine that models adversary behavior and potential attack paths. Machine learning–based predictive analytics support anomaly detection, risk scoring, and dynamic scenario generation within the simulation environment. Through simulated attack scenarios, the study evaluates the framework’s impact on detection speed, response coordination, and risk prioritization, demonstrating improved situational awareness and reduced exposure to advanced persistent threats. The results highlight that tightly integrated automation, intelligence fusion, and scenario-based simulation can significantly enhance operational resilience and inform risk-based security investment decisions for next-generation cyber defense.