Abstract
The integration of advanced technologies like artificial intelligence (AI) and Industrial Internet of Things (IIoT) has paved the way for innovative solutions in the food industry, including the development of digital twins for enterprises. This study explores the application of log analysis techniques to enhance the cybersecurity and operational efficiency of a digital twin platform for food industry enterprises. Specifically, it investigates methods for identifying Distributed Denial of Service (DDoS) attacks and monitoring user behavior to ensure the reliability and security of the digital twin ecosystem. Log analysis is employed to detect anomalies and patterns indicative of cyber threats, ensuring uninterrupted operations of critical systems. By integrating AI-driven algorithms with IIoT frameworks, the proposed approach not only mitigates risks associated with cyberattacks but also provides insights into user interactions, enabling predictive analytics and optimized decision-making. This research contributes to the advancement of secure, intelligent, and resilient digital twin architectures, offering significant benefits for improving productivity, reducing downtime, and safeguarding sensitive data in the food industry.