ARTIFICIAL INTELLIGENCE IN CYBERSECURITY: A DECADE OF RESEARCH TRENDS AND DEVELOPMENTS THROUGH BIBLIOMETRIC ANALYSIS

This study presents a comprehensive bibliometric analysis of the research landscape at the intersection of Artificial Intelligence (AI) and cybersecurity over the period 2017–2024. With an emphasis on deep learning (DL) and natural language processing (NLP), the analysis utilizes data sourced from the Scopus database and analyzed through VOSviewer and Biblioshiny. The dataset includes 2,692 publications, revealing a steep growth in scholarly output since 2018 and highlighting key contributions, collaborative networks, and influential research themes. Keyword co-occurrence and thematic mapping show that while deep learning and intrusion detection remain dominant, emerging areas such as federated learning and adversarial machine learning indicate a shift towards decentralized and adaptive cybersecurity approaches. The findings underline the dynamic and interdisciplinary nature of AI-driven cybersecurity research, offering vital insights for scholars, industry experts, and policymakers to shape future directions in this fast-evolving field