Anomaly Detection in <scp>IoT</scp> Environments Using Machine Learning: A Bibliometric Review, Challenges, and Future Research Directions

ABSTRACT The rapid proliferation of Internet of Things (IoT) systems has underscored the critical need for robust security measures to safeguard interconnected devices and data. This study presents an extensive bibliometric analysis of research advancements in anomaly detection in an IoT environment, leveraging data from the Web of Science repository to comprehend key trends, influential contributors, and evolving research themes. The analysis identifies the most prolific organizations, authors, and countries contributing to IoT anomaly detection literature, highlighting global scientific production and collaboration networks. The study traces publication trends, revealing the temporal distribution of article production and the impact of locally and globally cited sources. It also examines the most relevant authors in the field, their scholarly influence, and the dynamics of their research output over time. The co‐occurrence of authors' keywords provides insights into emerging themes and the evolution of research focus areas. At the same time, a detailed review of the most globally cited articles elucidates foundational contributions to the field. Additionally, the study analyzes the frequency and evolution of key terms, identifying trending topics that shape current and future research. The authors' and countries' collaboration networks illustrate the extent of international cooperation, highlighting key partnerships driving innovation. The application areas, challenges, and future research directions are also discussed, offering valuable guidance for further research. This bibliometric analysis offers a valuable resource for researchers and practitioners seeking to understand this domain's development, current state, and future research trajectory.