Cyber Twin of Smart Manufacturing System for Cybersecurity

Manufacturing plants are an integral part of critical infrastructure, ensuring the efficient and timely supply of goods and materials. To improve automation, productivity, and reliability, modern manufacturing plants increasingly integrate intelligent devices for real-time monitoring and control. However, like other modern industrial control systems, this digital transformation introduces cyber risks, making these plants vulnerable to attacks that could disrupt national supply chains.
This project focuses on developing a digital twin—a virtual replica of a modernized manufacturing plant that can be used for cybersecurity training, iterative cyber risk assessment, research, and forensics without affecting the operation of the real plant. By accurately replicating the plant’s cyber-physical infrastructure and processes, the digital twin enables controlled cybersecurity experiments, incident response, and the evaluation of risk mitigation strategies.
In this project we will build a virtual replica of a real-world manufacturing plant by analyzing the design and configuration of modernized manufacturing plants. By further understanding the attack surfaces and vectors in these environments, we will design optimized cybersecurity measures tailored to modern manufacturing plants.
This cyber-physical system model will serve as a valuable platform for cybersecurity applications. We will explore its use in implementing advanced cybersecurity technologies, including anomaly detection to identify deviations from normal operations, intrusion detection to monitor for malicious activities, and cyber deception techniques such as honeypots to mislead attackers.