Abstract Cybercriminals have increasingly adopted advanced and cutting-edge methods that expand the scale and speed of their attacks in recent years. This trend coincides with the rising demand for and scarcity of highly skilled cybersecurity specialists, making them both expensive and difficult to find. Recently, researchers have demonstrated the effectiveness of Artificial Intelligence (AI) approaches in combating sophisticated cyberattacks. However, comprehensive bibliometric data illustrating the study of AI approaches in cyberattack detection remain sparse. This study addresses this gap by investigating the current state of AI-based cyberattack detection research. The study analyzed the Scopus database using bibliometric analysis on a pool of over 2,338 articles published between 2014 and 2024, including 1217 journal articles, 828 conference papers, 121 conference reviews, 85 book chapters, 70 reviews, 5 editorials, and 2 books and short surveys. The study explores various AI-based cyberattack detection approaches globally, focusing on machine learning and deep learning algorithms. The bibliometric analysis was conducted using R, an open-source statistical tool, and
📖 افتح في inklap 🔗 DOI 📮 اطلب بحثاً