A bibliometric study toward quantitative research assessment of security of machine learning
Abstract
Purpose
Advances in machine learning (ML) have made significant contributions to the development of intelligent and autonomous systems leading to concerns about resilience of such systems against cyberattacks. This paper aims to report findings from a quantitative analysis of literature within ML security to assess current research trends in ML security.
Design/methodology/approach
The study focuses on statistical analysis of literature published between 2000 and 2023, providing quantitative research contributions targeting authors, countries and interdisciplinary studies of organizations. This paper reports existing surveys and a comparison of publications of attacks on ML and its in-demand security. Furthermore, an in-depth study of keywords, citations and collaboration is presented to facilitate deeper analysis of this literature.
Findings
Trends identified between 2021 and 2022 highlight an increase in focus on adversarial ML – 40\% more publications compared to 2020–2022 with more than 90\% publications in journals. This paper has also identified trends with respect to citations, keywords analysis, annual publications, co-author citations and geographical collaboration highlighting China and the USA as the countries with highest publications count and Biggio B. as the researcher with collaborative strength of 143 co-authors which highlight significant pollination of ideas and knowledge. Keyword analysis highlighted deep learning and computer vision as the most common domains for adversarial attacks due to the potential to perturb images whilst being challenging to identify issues in deep learning because of complex architecture.
Originality/value
The study presented in this paper identifies research trends, author contributions and open research challenges that can facilitate further research in this domain.
Keywords
- Adversarial machine learning
- Cyber threats
- Privacy preservation
- Secure machine learning
- Bibliometrics
- Quantitative analysis
- Analytical study
- Adversarial attack vectors
- Poisoning machine learning
- Evasion attacks
- Test-time attacks
- Differential privacy
- Data sanitization
- Adversarial re-training
- Data perturbation
Acknowledgements
Competing interests: The authors of this research publication declare no financial and non-financial interests in terms of publishing this research paper.
Ethical and informed consent for the data used: This literature analysis is solely based on the gathered data from Scopus for which no additional permissions are required.
Data availability and access: The compiled and refined data and results of this manuscript should be available upon request by the authors.
Citation
Paracha, A. and Arshad, J. (2024), "A bibliometric study toward quantitative research assessment of security of machine learning", Information Discovery and Delivery, Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/IDD-01-2024-0003
Publisher
:Emerald Publishing Limited
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