Smart-Grid Cyber-Attack and Disturbance Detection via Interpretable and Accurate Genetic-Fuzzy Data-Mining/ Machine-Learning Approach.
Saved in:
| Title: | Smart-Grid Cyber-Attack and Disturbance Detection via Interpretable and Accurate Genetic-Fuzzy Data-Mining/ Machine-Learning Approach. |
|---|---|
| Authors: | Gorzałczany, Marian B.1 (AUTHOR), Rudziński, Filip1 (AUTHOR) f.rudzinski@tu.kielce.pl |
| Source: | Energies (19961073). Jul2026, Vol. 19 Issue 14, p3388. 19p. |
| Subject Terms: | *Smart power grids, *Internet security, *Data mining, *Multi-objective optimization, *Fuzzy logic, *Feature selection, *Machine learning |
| Abstract: | This paper makes a contribution to smart-grid cybersecurity by proposing an application of our approach, previously published in this journal, to design—from smart-grid data—transparent, interpretable, and accurate systems for detection and classification of cyber-attacks, natural-event-related disturbances, and regular operation of the smart grid. The collection of 15 open-source simulated smart-grid data sets provided by Mississippi State University, USA, in collaboration with Oak Ridge National Laboratories, USA, is used in our experiments. To address this problem, we use our knowledge-based data-mining/machine-learning approach which ultimately generates a set of fuzzy rule-based classifiers—each with a different trade-off between its accuracy and the interpretability of its knowledge base (both are the subjects of maximization in a multi-objective optimization process using evolutionary algorithms). The paper also contributes an extensive cross-validation-based experiment showing that while our approach is not as accurate as alternative and inherently accuracy-oriented "black boxes," it surpasses them in terms of transparency and interpretability of the generated classification decisions, while ensuring acceptable accuracy of these decisions. Therefore, it can act as a second cybersecurity layer that uses transparent rules to help SCADA operators instantly verify and explain alerts triggered by accurate, fast "black-box" detectors. This paper also provides a contribution to a more general and important area of the automatic and effective selection of input variables (features) that are essential in a given decision-making problem (such as smart-grid contingency classification considered in this work). [ABSTRACT FROM AUTHOR] |
| Database: | Energy & Power Source |
|
Full text is not displayed to guests.
Login for full access.
|
|
| FullText | Links: – Type: pdflink Text: Availability: 1 |
|---|---|
| Header | DbId: enr DbLabel: Energy & Power Source An: 195801992 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
| IllustrationInfo | |
| Items | – Name: Title Label: Title Group: Ti Data: Smart-Grid Cyber-Attack and Disturbance Detection via Interpretable and Accurate Genetic-Fuzzy Data-Mining/ Machine-Learning Approach. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Gorzałczany%2C+Marian+B%2E%22">Gorzałczany, Marian B.</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Rudziński%2C+Filip%22">Rudziński, Filip</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> f.rudzinski@tu.kielce.pl</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Energies+%2819961073%29%22">Energies (19961073)</searchLink>. Jul2026, Vol. 19 Issue 14, p3388. 19p. – Name: Subject Label: Subject Terms Group: Su Data: *<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="%22Data+mining%22">Data mining</searchLink><br />*<searchLink fieldCode="DE" term="%22Multi-objective+optimization%22">Multi-objective optimization</searchLink><br />*<searchLink fieldCode="DE" term="%22Fuzzy+logic%22">Fuzzy logic</searchLink><br />*<searchLink fieldCode="DE" term="%22Feature+selection%22">Feature selection</searchLink><br />*<searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: This paper makes a contribution to smart-grid cybersecurity by proposing an application of our approach, previously published in this journal, to design—from smart-grid data—transparent, interpretable, and accurate systems for detection and classification of cyber-attacks, natural-event-related disturbances, and regular operation of the smart grid. The collection of 15 open-source simulated smart-grid data sets provided by Mississippi State University, USA, in collaboration with Oak Ridge National Laboratories, USA, is used in our experiments. To address this problem, we use our knowledge-based data-mining/machine-learning approach which ultimately generates a set of fuzzy rule-based classifiers—each with a different trade-off between its accuracy and the interpretability of its knowledge base (both are the subjects of maximization in a multi-objective optimization process using evolutionary algorithms). The paper also contributes an extensive cross-validation-based experiment showing that while our approach is not as accurate as alternative and inherently accuracy-oriented "black boxes," it surpasses them in terms of transparency and interpretability of the generated classification decisions, while ensuring acceptable accuracy of these decisions. Therefore, it can act as a second cybersecurity layer that uses transparent rules to help SCADA operators instantly verify and explain alerts triggered by accurate, fast "black-box" detectors. This paper also provides a contribution to a more general and important area of the automatic and effective selection of input variables (features) that are essential in a given decision-making problem (such as smart-grid contingency classification considered in this work). [ABSTRACT FROM AUTHOR] |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=enr&AN=195801992 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.3390/en19143388 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 19 StartPage: 3388 Subjects: – SubjectFull: Smart power grids Type: general – SubjectFull: Internet security Type: general – SubjectFull: Data mining Type: general – SubjectFull: Multi-objective optimization Type: general – SubjectFull: Fuzzy logic Type: general – SubjectFull: Feature selection Type: general – SubjectFull: Machine learning Type: general Titles: – TitleFull: Smart-Grid Cyber-Attack and Disturbance Detection via Interpretable and Accurate Genetic-Fuzzy Data-Mining/ Machine-Learning Approach. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Gorzałczany, Marian B. – PersonEntity: Name: NameFull: Rudziński, Filip IsPartOfRelationships: – BibEntity: Dates: – D: 15 M: 07 Text: Jul2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 19961073 Numbering: – Type: volume Value: 19 – Type: issue Value: 14 Titles: – TitleFull: Energies (19961073) Type: main |
| ResultId | 1 |
