Abstract: This paper demonstrates an innovative four-layer cybersecurity framework specifically developed for digital twin environments in the food industry. The suggested architecture integrates machine learning-based real-time anomaly detection (including large language patterns), blockchain-based data integrity, and process mining for advanced operational control. The system was examined in a simulated dairy processing plant with real industrial control system (ICS) components. Notable enhancements were observed, attaining 95% attack detection rate, minimizing false positives to 5%, and lowering an average threat response time to 200 milliseconds. These outcomes demonstrate the effectiveness of integrated highly developed AI algorthims and blockchain technology to preserve digital twin systems in critical production environments. The present method offers a scalable and adaptable approach to address traditional and emerging cybersecurity threats in the food industry sector.

Keywords: Digital Twin, Cybersecurity, Industrial Control Systems, Cyber-Physical Systems, Anomaly Detection, Machine Learning, Blockchain, Process Mining, Large Language Models

Pages: 132-143