DiGraph enabled Digital Twin and Label-Encoding Machine Learning for SCADA Network's Cyber Attack Analysis in Industry 5.0

False-Data Injection Attack (FDIA), Remote-Tripping Command Injection (RTCI), and System Reconfiguration Attack (SRA) on SCADA (Supervisory Control and Data Acquisition) networks impact industry 5.0 enabled smart grid components such as intelligent-electronic-device (IED), circuit-breaker, network-switch, and power transmission lines. Since the SCADA-network-based cyber-attacking flow is not in digital-twin form, it is impossible to simulate the effects of the attack. Furthermore, the string nature of these affected components’ data makes it challenging to incorporate into machine-learning-enabled intelligence (CTI) processes. To visualize the attacking flow of FDIA, RTCI, and SRA cyber-attacks on SCADA networks, this paper presents a novel “Digital Twin and Machine Learning empowered Cyber Attacking Flow Analysis (DT-ML-CAFA)” approach for grid CTI in Industry 5.0. To process digital twins and determine how the cyberattacks are impacting SCADA components, the directed-graph (DiGraph) algorithm-based knowledge-graph method is utilized. The overall digital-twin process is examined using machine learning techniques based on Extra-Trees, Random-Forest, Bootstrap-Aggregating (Bagging), XGBoost, and Logistic-Regression. Based on the experimental results of this study, this paper shows that the proposed method can simulate the flow of cyber-attacks on the SCADA network in the form of the digital twin, and the confusion metrics of the digital twin are obtained with high accuracy.