AI-POWERED CYBERSECURITY IN THE CLOUD ERA: A BIBLIOMETRIC ANALYSIS OF RESEARCH TRENDS FROM 2017 TO 2024

: Over the past decade, artificial intelligence (AI) has emerged as a transformative force in cybersecurity, particularly within cloud environments. Traditional security approaches struggle to keep pace with complex, evolving threats—making AI-driven models crucial for real-time detection and adaptive response. This paper presents a bibliometric analysis of 2,692 publications indexed in the Scopus database between 2017 and 2024, focusing on research integrating deep learning (DL) and natural language processing (NLP) within cybersecurity. Using VOSviewer and Biblioshiny, we explore thematic evolution, keyword trends, and collaboration networks. Our results highlight a surge in scholarly output post-2018, with deep learning and intrusion detection as central research themes. Emerging areas such as federated learning and adversarial machine learning indicate a growing emphasis on scalable, privacy-aware AI solutions. This study offers valuable insights for researchers and practitioners navigating the evolving intersection of AI and cybersecurity.

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Publication Details

Journal
International Journal of Advanced Research in Computer Science
Published
2025-08-20
DOI
https://doi.org/10.26483/ijarcs.v16i4.7304
Primary Topic
Network Security and Intrusion Detection
Type
article
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article

AI-POWERED CYBERSECURITY IN THE CLOUD ERA: A BIBLIOMETRIC ANALYSIS OF RESEARCH TRENDS FROM 2017 TO 2024

S. Suganya
1 links
International Journal of Advanced Research in Computer Science
Network Security and Intrusion Detection
article

AI-POWERED CYBERSECURITY IN THE CLOUD ERA: A BIBLIOMETRIC ANALYSIS OF RESEARCH TRENDS FROM 2017 TO 2024

S. Suganya
article en

Abstract

: Over the past decade, artificial intelligence (AI) has emerged as a transformative force in cybersecurity, particularly within cloud environments. Traditional security approaches struggle to keep pace with complex, evolving threats—making AI-driven models crucial for real-time detection and adaptive response. This paper presents a bibliometric analysis of 2,692 publications indexed in the Scopus database between 2017 and 2024, focusing on research integrating deep learning (DL) and natural language processing (NLP) within cybersecurity. Using VOSviewer and Biblioshiny, we explore thematic evolution, keyword trends, and collaboration networks. Our results highlight a surge in scholarly output post-2018, with deep learning and intrusion detection as central research themes. Emerging areas such as federated learning and adversarial machine learning indicate a growing emphasis on scalable, privacy-aware AI solutions. This study offers valuable insights for researchers and practitioners navigating the evolving intersection of AI and cybersecurity.

International Journal of Advanced Research in Computer ScienceVol. 16(4)
La Maddalena (IT)
Openalex Percentile: Top 75%
Network Security and Intrusion Detection
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