Abstract
Background
The relentless surge and growing frequency of cyber threats have indicated that traditional cybersecurity systems are ineffective. The need for more vigorous measures to safeguard information systems has never been more critical. This dilemma underscores the urgent need for advanced, adaptive cybersecurity solutions to detect and proactively counter these sophisticated threats. The study aims to investigate the game-changing role of machine learning in advancing cybersecurity through an in-depth scientometrics and bibliometric analysis. The study aims to map the current research landscape, identify significant contributions, discover emerging trends, and underscore key advancements in machine learning-based cybersecurity practices.
Methods
The Scopus database was used to conduct bibliometric and scientometric analyses of the machine learning and cybersecurity literature published from 2010 to 2024. Advanced tools were employed for scientometric analysis to evaluate scholarly output, authors’ impact, and the co-occurrence of keywords across geographical, organisational, and thematic indicators.
Results
The study found that India remains at the top in publication count, with IEEE Access as the leading journal and Princess Nourah Bint Abdul Rahman University as the most productive institution in machine learning and cybersecurity research. The study finds that Alazab, M., and Rao, R. are the most dominant authors. The findings revealed a significant increase in scholarly output since 2013, with intrusion detection, cybercrime prevention, and machine learning techniques identified as the most prominent themes.
Conclusions
The study highlights the significant role of ML in deriving next-generation cybersecurity solutions. The results could empower practitioners and researchers to establish a proactive, machine-learning-driven cybersecurity infrastructure. Future research should emphasise collaboration with other disciplines, including the social and psychological aspects of cyber threats.
The relentless surge and growing frequency of cyber threats have indicated that traditional cybersecurity systems are ineffective. The need for more vigorous measures to safeguard information systems has never been more critical. This dilemma underscores the urgent need for advanced, adaptive cybersecurity solutions to detect and proactively counter these sophisticated threats. The study aims to investigate the game-changing role of machine learning in advancing cybersecurity through an in-depth scientometrics and bibliometric analysis. The study aims to map the current research landscape, identify significant contributions, discover emerging trends, and underscore key advancements in machine learning-based cybersecurity practices.
Methods
The Scopus database was used to conduct bibliometric and scientometric analyses of the machine learning and cybersecurity literature published from 2010 to 2024. Advanced tools were employed for scientometric analysis to evaluate scholarly output, authors’ impact, and the co-occurrence of keywords across geographical, organisational, and thematic indicators.
Results
The study found that India remains at the top in publication count, with IEEE Access as the leading journal and Princess Nourah Bint Abdul Rahman University as the most productive institution in machine learning and cybersecurity research. The study finds that Alazab, M., and Rao, R. are the most dominant authors. The findings revealed a significant increase in scholarly output since 2013, with intrusion detection, cybercrime prevention, and machine learning techniques identified as the most prominent themes.
Conclusions
The study highlights the significant role of ML in deriving next-generation cybersecurity solutions. The results could empower practitioners and researchers to establish a proactive, machine-learning-driven cybersecurity infrastructure. Future research should emphasise collaboration with other disciplines, including the social and psychological aspects of cyber threats.
| Original language | English |
|---|---|
| Article number | 276 |
| Journal | F1000Research |
| Volume | 15 |
| Early online date | 16 Feb 2026 |
| DOIs | |
| Publication status | Published - 16 Feb 2026 |
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