Academic Journal
Advancing Cybersecurity Through Machine Learning: A Scientometric Analysis of Global Research Trends and Influential Contributions.
| Title: | Advancing Cybersecurity Through Machine Learning: A Scientometric Analysis of Global Research Trends and Influential Contributions. |
|---|---|
| Authors: | Razzaq, Kamran, Shah, Mahmood |
| Source: | Journal of Cybersecurity & Privacy; Jun2025, Vol. 5 Issue 2, p12-N.PAG, 25p |
| Subject Terms: | Citation networks, Bibliometrics, Databases, Machine learning, Citation analysis, Deep learning |
| Abstract: | Implementing machine learning is imperative for enhancing advanced cybersecurity practices globally. The current cybersecurity landscape needs further investigation into the potential impasse. This scientometric study aims to comprehensively analyse the study patterns and key contributions at the nexus of cybersecurity and machine learning. The analysis examines publication trends, citation analysis, and intensive research networks to discover key authors, significant organisations, major countries, and emerging research areas. The search was conducted on the Scopus database, and 3712 final documents were selected after a thorough screening from January 2016 to January 2025. The VOSviewer tool was used to map citation networks and visualise co-authorship networks, enabling the discovery of research patterns, top contributors, and hot topics in the domain. The findings uncovered the substantial growth in publications bridging cybersecurity with machine learning and deep learning, involving 2865 authors across 160 institutions and 114 countries. Saudi Arabia emerged as a top contributing nation with flaunting high productivity. IEEE and Sensors are the key publication sources instrumental in producing interdisciplinary research. Iqbal H. Sarker and N. Moustafa are notable authors, with 17 and 16 publications each. This study emphasises the significance of global partnerships and multidisciplinary research in enhancing cybersecurity posture and identifying key research areas for future studies. This study further highlights its importance by guiding policymakers and practitioners to develop advanced machine learning-based cybersecurity strategies. [ABSTRACT FROM AUTHOR] |
| Copyright of Journal of Cybersecurity & Privacy is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Complementary Index |
| FullText | Text: Availability: 0 |
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| Header | DbId: edb DbLabel: Complementary Index An: 186286777 RelevancyScore: 1007 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 1007.33093261719 |
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| Items | – Name: Title Label: Title Group: Ti Data: Advancing Cybersecurity Through Machine Learning: A Scientometric Analysis of Global Research Trends and Influential Contributions. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Razzaq%2C+Kamran%22">Razzaq, Kamran</searchLink><br /><searchLink fieldCode="AR" term="%22Shah%2C+Mahmood%22">Shah, Mahmood</searchLink> – Name: TitleSource Label: Source Group: Src Data: Journal of Cybersecurity & Privacy; Jun2025, Vol. 5 Issue 2, p12-N.PAG, 25p – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Citation+networks%22">Citation networks</searchLink><br /><searchLink fieldCode="DE" term="%22Bibliometrics%22">Bibliometrics</searchLink><br /><searchLink fieldCode="DE" term="%22Databases%22">Databases</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Citation+analysis%22">Citation analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Deep+learning%22">Deep learning</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Implementing machine learning is imperative for enhancing advanced cybersecurity practices globally. The current cybersecurity landscape needs further investigation into the potential impasse. This scientometric study aims to comprehensively analyse the study patterns and key contributions at the nexus of cybersecurity and machine learning. The analysis examines publication trends, citation analysis, and intensive research networks to discover key authors, significant organisations, major countries, and emerging research areas. The search was conducted on the Scopus database, and 3712 final documents were selected after a thorough screening from January 2016 to January 2025. The VOSviewer tool was used to map citation networks and visualise co-authorship networks, enabling the discovery of research patterns, top contributors, and hot topics in the domain. The findings uncovered the substantial growth in publications bridging cybersecurity with machine learning and deep learning, involving 2865 authors across 160 institutions and 114 countries. Saudi Arabia emerged as a top contributing nation with flaunting high productivity. IEEE and Sensors are the key publication sources instrumental in producing interdisciplinary research. Iqbal H. Sarker and N. Moustafa are notable authors, with 17 and 16 publications each. This study emphasises the significance of global partnerships and multidisciplinary research in enhancing cybersecurity posture and identifying key research areas for future studies. This study further highlights its importance by guiding policymakers and practitioners to develop advanced machine learning-based cybersecurity strategies. [ABSTRACT FROM AUTHOR] – Name: Abstract Label: Group: Ab Data: <i>Copyright of Journal of Cybersecurity & Privacy is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.3390/jcp5020012 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 25 StartPage: 12 Subjects: – SubjectFull: Citation networks Type: general – SubjectFull: Bibliometrics Type: general – SubjectFull: Databases Type: general – SubjectFull: Machine learning Type: general – SubjectFull: Citation analysis Type: general – SubjectFull: Deep learning Type: general Titles: – TitleFull: Advancing Cybersecurity Through Machine Learning: A Scientometric Analysis of Global Research Trends and Influential Contributions. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Razzaq, Kamran – PersonEntity: Name: NameFull: Shah, Mahmood IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 06 Text: Jun2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 2624800X Numbering: – Type: volume Value: 5 – Type: issue Value: 2 Titles: – TitleFull: Journal of Cybersecurity & Privacy Type: main |
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