Digital Twin: An emerging tool with Machine learning to detect real world cyber-attacks and anomaly in the Industry 4.0 manufacturing system

As a result of the extensive adoption of Industry 4.0 manufacturing systems, two major problems have arisen: cybersecurity and anomaly detection. The initial part of the study involves an examination of potential cybersecurity threats, followed by the introduction of a dataset generated from a real and contemporary system. Subsequent sections present a novel approach that leverages the capabilities of both digital twin and machine learning technologies to design intrusion detection systems for cybersecurity attacks and identify anomalies within Industry 4.0 systems. The study further introduces domain adaptation techniques to increase the effectiveness of Machine learning models.