Cyber threat intelligence for smart grids using knowledge graphs, digital twins, and hybrid machine learning in SCADA networks
In the SCADA (Supervisory Control and Data Acquisition) network of a smart grid, the network switch is connected to multiple Intelligent Electronic Devices (IEDs) that are based on protective relays. False-Data Injection Attacks (FDIA), Remote-Tripping Command Injection (RTCI), and System Reconfiguration Attacks (SRA) are three types of cyber-attacks on SCADA networks, resulting in single-line-to-ground (SLG) fault, IED-relay failure, and circuit-breaker open issues occur. The existing cyber threat intelligence (CTI) approaches of grids are unable to provide visualization of cyber-attacking grid effects. To understand the full effect of the attacks, there is a need for a knowledge-graph method-based digital-twin cyber-attack visualization approach in SCADA networks, which is missing in existing SCADA systems. This study presents a novel “Digital-twin and Machine Learning-based SCADA Cyber Threat Intelligence (DT-ML-SCADA-CTI)” approach, which utilizes an innovative algorithm to visualize and predict the effects of cyber-attacks, including FDIA, RTCI, and SRA, on SCADA systems. The process begins with data transformation to generate cyber-attack grid data, which is then analyzed for attack prediction using machine learning models such as Extra-Trees, XGBoost, Random Forest, Bootstrap Aggregating, and Logistic Regression. To further enhance the analysis, a directed-graph (DiGraph) algorithm is applied to create a knowledge-graph-based digital twin, allowing for a deeper understanding of how these cyber-attacks impact SCADA operations. The comparison with existing models demonstrates the superiority of the proposed approach, as it offers a more detailed and clearer digital-twin representation of cyber-attack effects. This enhanced visualization provides deeper insights into attack dynamics and significantly improves predictive accuracy, showcasing the effectiveness of the proposed method in understanding and mitigating cyber threats.
Authors
- Anoud Bani-Hani (ORCID: https://orcid.org/0000-0002-0782-1691)
- Hussam Al Hamadi (ORCID: https://orcid.org/0000-0002-1353-4138)
- Nabeel Al-Qirim (ORCID: https://orcid.org/0000-0002-0090-7521)
- Munir Majdalawieh (ORCID: https://orcid.org/0000-0002-2559-7371)
Institutions
- University of Dubai (AE)
- Zayed University (AE)
Publication Details
- Journal
- International Journal of Engineering Business Management
- Published
- 2025-03-25
- DOI
- https://doi.org/10.1177/18479790251328183
- Citations
- 2
- Primary Topic
- Smart Grid Security and Resilience
- Type
- article
- Field-Weighted Citation Impact
- 2.43
Funders
- Zayed University