Exploring the landscape of cryptocurrency and security research: A bibliometric study enhanced by LLM-based affiliation harmonization

Abstract

Over the past decade, decentralized digital currencies have gained prominence in finance and technology, but their growth has also drawn adversaries exploiting security vulnerabilities. This paper reviews the literature on cryptocurrency and security using bibliometric analysis of Web of Science and Scopus articles published between 2013 and May 2025. Tools such as Biblioshiny and VOSviewer were employed to explore key trends, influential contributors, collaborative networks, and emerging themes. A novel contribution of this study is the use of Large Language Models (LLMs) to address inconsistent affiliation formats, enabling accurate identification of leading academic organizations. The results demonstrate that LLM-based harmonization effectively prevents misrepresentation in bibliometric datasets. Overall, this study not only summarizes evolving research trends in cryptocurrency and security but also highlights the potential of LLMs to enhance bibliometric methods, suggesting broader applications for improving the accuracy and reliability of future scholarly analyses.