Blockchain-Enhanced Cybersecurity Framework for Industry 4.0 Smart Grids: A Machine Learning-Based Intrusion Detection Approach.
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| Title: | Blockchain-Enhanced Cybersecurity Framework for Industry 4.0 Smart Grids: A Machine Learning-Based Intrusion Detection Approach. |
|---|---|
| Authors: | Mahboob, Asrar1 (AUTHOR), Rashad, Muhammad1,2 (AUTHOR), Awan, Ahmed Bilal2,3 (AUTHOR) a.awan@ajman.ac.ae, Abbas, Ghulam1 (AUTHOR) ghulam.abbas@ee.uol.edu.pk |
| Source: | Energies (19961073). May2026, Vol. 19 Issue 9, p2202. 35p. |
| Subject Terms: | *Blockchains, *Machine learning, *Smart power grids, *Internet security, *Denial of service attacks, *Industry 4.0, *Intrusion detection systems (Computer security), *Energy infrastructure |
| Abstract: | Recent years have witnessed the rapid proliferation of Industry 4.0 technologies in smart grids, leading to a revolution in energy generation and management, which provides improved operational efficiency and intelligent automation for smart grids. Nevertheless, this highly integrated infrastructure, while making energy more secure and reliable, simultaneously creates greater vulnerability to sophisticated cyber threats such as Distributed Denial of Service (DDoS) attacks, data manipulation and unauthorized access. The task of addressing these challenges requires innovative approaches that maintain the resilience as well as security of critical energy infrastructures. A novel Blockchain-Enhanced Cybersecurity Framework (BCF) specific to Industry 4.0-enabled smart grid systems is presented in this paper. The proposed framework integrates advanced security protocols with real-time threat detection capabilities through the decentralized, transparent and tamper-resistant nature of blockchain technology. Authentication, data validation and secure communication are accomplished through smart contracts to automate it, eliminating human intervention and single points of failures. The framework is able to allow for high transaction volumes, typical of modern smart grid networks, whilst maintaining integrity via a hybrid consensus mechanism that ensures scalability. In addition, the framework is further augmented with a Machine Learning-Based Intrusion Detection System (ML-IDS) to detect and mitigate cyber-attacks in real time. The proposed system achieves excellent performance in identifying malicious activities with high accuracy, precision and recall on the UNSW-NB15 dataset. Analysis with traditional methods indicates that the Blockchain Enhanced Cybersecurity Framework significantly lowers false positive rates and increases detection reliability. The framework is justified in terms of its strength to secure the systems in Industry 4.0-enabled smart grids against emerging cyber threats through extensive simulations and case studies. The value of this work is that it shows that blockchain and machine learning can be used to improve cybersecurity in renewable energy systems, and concrete insights and recommendations on implementing secure and cost-effective systems of energy infrastructure are provided. The proposed framework creates an enabling environment on which the creation of resilient and future-ready smart grids to facilitate the global goal of sustainable and secure energy can be developed. [ABSTRACT FROM AUTHOR] |
| Database: | Energy & Power Source |
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| FullText | Links: – Type: pdflink Text: Availability: 1 |
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| Header | DbId: enr DbLabel: Energy & Power Source An: 193716098 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Blockchain-Enhanced Cybersecurity Framework for Industry 4.0 Smart Grids: A Machine Learning-Based Intrusion Detection Approach. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Mahboob%2C+Asrar%22">Mahboob, Asrar</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Rashad%2C+Muhammad%22">Rashad, Muhammad</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Awan%2C+Ahmed+Bilal%22">Awan, Ahmed Bilal</searchLink><relatesTo>2,3</relatesTo> (AUTHOR)<i> a.awan@ajman.ac.ae</i><br /><searchLink fieldCode="AR" term="%22Abbas%2C+Ghulam%22">Abbas, Ghulam</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> ghulam.abbas@ee.uol.edu.pk</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Energies+%2819961073%29%22">Energies (19961073)</searchLink>. May2026, Vol. 19 Issue 9, p2202. 35p. – Name: Subject Label: Subject Terms Group: Su Data: *<searchLink fieldCode="DE" term="%22Blockchains%22">Blockchains</searchLink><br />*<searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br />*<searchLink fieldCode="DE" term="%22Smart+power+grids%22">Smart power grids</searchLink><br />*<searchLink fieldCode="DE" term="%22Internet+security%22">Internet security</searchLink><br />*<searchLink fieldCode="DE" term="%22Denial+of+service+attacks%22">Denial of service attacks</searchLink><br />*<searchLink fieldCode="DE" term="%22Industry+4%2E0%22">Industry 4.0</searchLink><br />*<searchLink fieldCode="DE" term="%22Intrusion+detection+systems+%28Computer+security%29%22">Intrusion detection systems (Computer security)</searchLink><br />*<searchLink fieldCode="DE" term="%22Energy+infrastructure%22">Energy infrastructure</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Recent years have witnessed the rapid proliferation of Industry 4.0 technologies in smart grids, leading to a revolution in energy generation and management, which provides improved operational efficiency and intelligent automation for smart grids. Nevertheless, this highly integrated infrastructure, while making energy more secure and reliable, simultaneously creates greater vulnerability to sophisticated cyber threats such as Distributed Denial of Service (DDoS) attacks, data manipulation and unauthorized access. The task of addressing these challenges requires innovative approaches that maintain the resilience as well as security of critical energy infrastructures. A novel Blockchain-Enhanced Cybersecurity Framework (BCF) specific to Industry 4.0-enabled smart grid systems is presented in this paper. The proposed framework integrates advanced security protocols with real-time threat detection capabilities through the decentralized, transparent and tamper-resistant nature of blockchain technology. Authentication, data validation and secure communication are accomplished through smart contracts to automate it, eliminating human intervention and single points of failures. The framework is able to allow for high transaction volumes, typical of modern smart grid networks, whilst maintaining integrity via a hybrid consensus mechanism that ensures scalability. In addition, the framework is further augmented with a Machine Learning-Based Intrusion Detection System (ML-IDS) to detect and mitigate cyber-attacks in real time. The proposed system achieves excellent performance in identifying malicious activities with high accuracy, precision and recall on the UNSW-NB15 dataset. Analysis with traditional methods indicates that the Blockchain Enhanced Cybersecurity Framework significantly lowers false positive rates and increases detection reliability. The framework is justified in terms of its strength to secure the systems in Industry 4.0-enabled smart grids against emerging cyber threats through extensive simulations and case studies. The value of this work is that it shows that blockchain and machine learning can be used to improve cybersecurity in renewable energy systems, and concrete insights and recommendations on implementing secure and cost-effective systems of energy infrastructure are provided. The proposed framework creates an enabling environment on which the creation of resilient and future-ready smart grids to facilitate the global goal of sustainable and secure energy can be developed. [ABSTRACT FROM AUTHOR] |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=enr&AN=193716098 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.3390/en19092202 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 35 StartPage: 2202 Subjects: – SubjectFull: Blockchains Type: general – SubjectFull: Machine learning Type: general – SubjectFull: Smart power grids Type: general – SubjectFull: Internet security Type: general – SubjectFull: Denial of service attacks Type: general – SubjectFull: Industry 4.0 Type: general – SubjectFull: Intrusion detection systems (Computer security) Type: general – SubjectFull: Energy infrastructure Type: general Titles: – TitleFull: Blockchain-Enhanced Cybersecurity Framework for Industry 4.0 Smart Grids: A Machine Learning-Based Intrusion Detection Approach. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Mahboob, Asrar – PersonEntity: Name: NameFull: Rashad, Muhammad – PersonEntity: Name: NameFull: Awan, Ahmed Bilal – PersonEntity: Name: NameFull: Abbas, Ghulam IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 05 Text: May2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 19961073 Numbering: – Type: volume Value: 19 – Type: issue Value: 9 Titles: – TitleFull: Energies (19961073) Type: main |
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