A Comprehensive Bibliometric Analysis of AI-Driven Crime Detection Research

Background: With the rapid integration of artificial intelligence (AI) in law enforcement, AI-driven crime detection has become an essential research area. This paper provides a bibliometric analysis of the field, examining publication patterns, geographic distribution, leading authors, prominent affiliations, and thematic focus. Methods: Using the PRISMA framework, publications from 2015 to 2024 were analyzed based on predefined inclusion criteria. The study synthesized data on publication count, citation metrics, and keyword occurrences to identify trends and influential contributors within the AI-driven crime detection domain. Results: The analysis revealed a significant increase in research output, with India, the United States, and China as leading contributors, reflecting a global commitment to AI in crime detection. Prominent authors such as Ban Tao and Nour Moustafa emerged, demonstrating high citation counts and H-index values indicative of the field's academic influence. Leading affiliations, including the SRM Institute of Science and Technology and the University of New South Wales, underscored the importance of institutional support in advancing research. Keyword analysis highlighted a shift from general topics like 'computer crime' to specific technologies such as 'machine learning' and 'deep learning,' reflecting the field's progression towards practical applications. Conclusion: AI-driven crime detection research is characterized by its rapid growth, diverse global contributions, and a shift towards specialized, technology-driven solutions. The increasing focus on machine learning and network security illustrates the field's response to the evolving landscape of digital crime, with implications for future research directions and policy-making.