Document Type
Article
Source of Publication
IEEE Open Journal of the Communications Society
Publication Date
1-1-2024
Abstract
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.
DOI Link
ISSN
Disciplines
Computer Sciences
Keywords
Cyber Security, Digital twin, Knowledge Graph, SCADA, Smart grid
Scopus ID
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Recommended Citation
Al-Qirim, Nabeel; Bani-Hani, Anoud; Majdalawieh, Munir; Hamadi, Hussam Al; and Hasan, Mohammad Kamrul, "DiGraph enabled Digital Twin and Label-Encoding Machine Learning for SCADA Network's Cyber Attack Analysis in Industry 5.0" (2024). All Works. 6935.
https://zuscholars.zu.ac.ae/works/6935
Indexed in Scopus
yes
Open Access
yes
Open Access Type
Gold: This publication is openly available in an open access journal/series