Mapping the Evolution of Intrusion Detection in Big Data: A Bibliometric Analysis

Abstract

This study provides a comprehensive analysis of the dynamic amalgamation of intrusion detection and big data, revealing trends and patterns within cybersecurity research. The investigation reveals a notable surge in scholarly output from 2018 onwards, reflecting heightened interest and exploration within the field. Dominant themes such as "intrusion detection," "big data," and "machine learning" underscore the integration of security concerns with advanced technologies. Geographical influences showcase diverse contributions, with varying citation impacts from countries like India, China, and Saudi Arabia. Author contributions reveal a balance between prolific authors and impactful contributions from authors with fewer publications. Recommendations include fostering interdisciplinary collaborations, integrating advanced computational methods, and conducting longitudinal studies to gauge sustained impacts. This research underscores collaboration dynamics, thematic evolution, and global influences as pivotal facets within the realm of intrusion detection and big data, guiding future research to fortify digital security in an ever-evolving technological landscape.