Abstract

Complex systems, such as smart grids, manufacturing plants, and autonomous transport networks, are becoming more digital. This improves operations but also creates weaknesses to cyberattacks. Traditional security approaches cannot always keep up with the real-time monitoring needed to detect and respond to new threats in these changing environments. This paper proposes using a Digital Twin (DT) architecture that is model-based to monitor security in complex systems. Digital Twin (DT) technology, along with Model-Based Systems Engineering (MBSE), lets us monitor security differently. The objective is to design a DT framework that mirrors a real-world system in a virtual environment. This allows for real-time analysis, detecting anomalies, and predicting how to react to security issues. The proposed architecture has three core layers: the Digital Twin Core, the MBSE Integration Layer, and the Security Layer. The architecture was implemented using MATLAB/Simulink for system modeling, Unity 3D for visualization, and Snort IDS for threat detection. The DT system was tested in a simulated industrial control system environment using OMNeT++ as the communication backbone and Kali Linux for launching common cyberattacks, such as data injection, spoofing, and replay attacks. The results showed that the DT architecture was able to detect threats with 96.5% accuracy. A comparative analysis show that the proposed model-based digital twin architecture improved detection accuracy by 18%, reduced false positives by 25%, and decreased detection latency by 32%. This work shows that a model-based DT architecture greatly improves how well security monitoring works in complex systems, making it more responsive and accurate. Future work will involve real-world deployment and integrating AI-driven prediction models for automatic threat mitigation.

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