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.

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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

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article

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

Anoud Bani-Hani, Hussam Al Hamadi, Nabeel Al-Qirim, Munir Majdalawieh
2 citations
International Journal of Engineering Business Management
Smart Grid Security and Resilience
2.43
article

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

Anoud Bani-Hani, Hussam Al Hamadi, Nabeel Al-Qirim, Munir Majdalawieh
article en
2 citations

Abstract

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.

International Journal of Engineering Business ManagementVol. 17
University of Dubai (AE), Zayed University (AE)
Zayed University
Openalex Percentile: Top 13%
Smart Grid Security and Resilience
2.43
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