Abstract:

The use of AI-driven digital twins in cybersecurity is a revolutionary approach to the identification, simulation, and response to threats in virtual environments. Intelligent clones of this nature can duplicate physical systems within live environments to enable real-time monitoring and preventive defense strategies. Current methods for countering cyber threats are mainly based on static models, rule-based systems, and look-back response mechanisms. First of all, these models do not react to the real-time dynamics of threats that are continuously changing. Further, the pre-programmed protocols introduce delays retrospectively while reacting to incidents, thus effectively slowing down active management, mitigation, and neutralization, which is real-time-based and is on-the-fly. All these inadequacies in their methodologies leave loopholes that make them effective against more advanced and developing cyber threats that take advantage of obsolete detection mechanisms and contextual blind filters. To address these challenges, this study suggests an IntelliSim-Twin: AI-Driven Cyber Threat Simulation and Mitigation Framework. The framework uses reinforcement learning and real-time anomaly detection together with predictive behavior modeling using digital twin environments. IntelliSim-Twin can be implemented on cloud-based infrastructures, IoT environments, and key network systems to facilitate real-time monitoring and automatic threat forecasting and mitigation planning. The experimental results demonstrate that IntelliSim-Twin has significantly improved responding to threats by streamlining response time, mitigating false positives, directing actionable resilience to the blueprints, and fine-tuning context-aware, AI-informed adjustments to defense strategies.The proposed method marks a substantial advancement in proactive cybersecurity, making it a valuable asset for safeguarding increasingly complex virtual infrastructures.