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

Aug 4, 2026

False-Data Injection Attack (FDIA), Remote-Tripping Command Injection (RTCI), and System Reconfiguration Attack (SRA) against SCADA networks can disrupt Industry 5.0 smart-grid components such as IEDs, circuit breakers, network switches, and power transmission lines. Because the cyber-attacking flow is not natively available in digital-twin form, effects cannot be directly simulated, and affected component data lacks straightforward structure for machine-learning-driven CTI. This paper introduces a digital-twin and ML enabled cyber-attacking flow analysis method that uses DiGraph-based knowledge-graph modeling and ML classifiers to obtain high-accuracy digital-twin confusion metrics.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages14

Reading Time35 min

AccessFree to download

Abstract

Attacks on SCADA in Industry 5.0

Digital twin and ML enabled cyber-attacking flow analysis

DiGraph-based knowledge-graph modeling

Machine learning models and evaluation

Zayed University ZU Scholars All Works 1-1-2024 DiGraph enabled Digital Twin and Label-Encoding Machine Learning for SCADA Network's Cyber Attack Analysis in Industry 5.0 Nabeel Al-Qirim Zayed University Anoud Bani-Hani Zayed University Munir Majdalawieh Zayed University Hussam Al Hamadi University of Dubai Mohammad Kamrul Hasan Universiti Kebangsaan Malaysia Follow this and additional works at: [https://zuscholars.zu.ac.ae/works](https://zuscholars.zu.ac.ae/works) Part of the Computer Sciences Commons 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](https://zuscholars.zu.ac.ae/works/6935) This Article is brought to you for free and open access by ZU Scholars. It has been accepted for inclusion in All Works by an authorized administrator of ZU Scholars. For more information, please contact [scholars@zu.ac.ae](scholars@zu.ac.ae). This article has been accepted for publication in IEEE Open Journal of the Communications Society. This is the author's version which has not been fully edited and content may change prior to final publication. Citation information: DOI 10. 1109/OJCOMS.2024.3502544 Received XX Month XXXX; revised X Month XXXX; accepted XX Month XXXX. Date of publication XX Month XXXX; date of current version XX Month XXXX. Digital Object Identifier 10. 1109/OJCOMS.2020.1234567 DiGraph enabled Digital Twin and Label-encoding Machine Learning for SCADA Network’s Cyber Attack Analysis in Industry 5.0 NABEEL AL-QIRIM1*, SENIOR MEMBER, ANOUD BANI-HANI1, MUNIR MAJDALAWIEH1, HUSSAM AL HAMADI2, SENIOR MEMBER, IEEE, MOHAMMAD KAMRUL HASAN3, SENIOR MEMBER, IEEE. 1College of Technological Innovation, Zayed University, UAE; (e-mail: [Nabeel.AlQirim@zu.ac.ae](Nabeel.AlQirim@zu.ac.ae); [Munir.Majdalawieh@zu.ac.ae](Munir.Majdalawieh@zu.ac.ae); [Anood.banihani@hotmail.com](Anood.banihani@hotmail.com)), 2College of Engineering and IT, University of Dubai, UAE,(e-mail: [halhammadi@ud.ac.ae](halhammadi@ud.ac.ae)) 3Centre for Cyber Security, Information Science & Technology, Universiti Kebangsaan Malaysia (UKM), Malaysia ([mkhasan@ukm.edu.my](mkhasan@ukm.edu.my)) CORRESPONDING AUTHOR: NABEEL AL-QIRIM (e-mail: [Nabeel.AlQirim@zu.ac.ae](Nabeel.AlQirim@zu.ac.ae)) . This work has been supported by the Zayed University Research Incentive Fund (RIF) research grant code number: R21109 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

FAQs

哪些类型的网络攻击影响了文中研究对象的SCADA网络？

文中关注FDIA、RTCI和SRA三类对SCADA网络的攻击，并说明其对Industry 5.0智能电网组件的影响。

为什么传统方式难以对SCADA网络攻击效果进行仿真？

因为SCADA网络的攻击流程并未以数字孪生形式呈现，导致难以直接模拟攻击影响。同时，受影响组件数据呈字符串性质，难以嵌入面向机器学习的CTI流程。

文中如何可视化并分析SCADA网络上的攻击流程？

作者提出“Digital Twin and Machine Learning empowered Cyber Attacking Flow Analysis (DT-ML-CAFA)”方法，使用基于DiGraph的知识图谱方法处理数字孪生，并通过机器学习模型评估数字孪生的准确性。

Aug 4, 2026

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