Cyber threat intelligence for smart grids using knowledge graphs, digital twins, and hybrid machine learning in SCADA networks

Nabeel Al-Qirim et al.

International Journal of Engineering Business Management2025https://doi.org/10.1177/18479790251328183article
AJG 1
Weight
0.41

What the paper says

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.

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https://doi.org/https://doi.org/10.1177/18479790251328183

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@article{nabeel2025,
  title        = {{Cyber threat intelligence for smart grids using knowledge graphs, digital twins, and hybrid machine learning in SCADA networks}},
  author       = {Nabeel Al-Qirim et al.},
  journal      = {International Journal of Engineering Business Management},
  year         = {2025},
  doi          = {https://doi.org/https://doi.org/10.1177/18479790251328183},
}

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Evidence weight

0.41

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.25 × 0.4 = 0.10
M · momentum0.55 × 0.15 = 0.08
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.