Trends of Machine Learning, Cybersecurity and Big Data Analytics in Industry 4.0

Abstract

This research explores the integration of Machine Learning (ML), Cybersecurity, and Big Data Analytics (BDA) in advancing intelligent, secure, and sustainable industrial ecosystems within Industry 4.0. It assesses global research productivity, collaboration patterns, and the connection between intelligent automation, data-driven innovation, and cyber resilience. A PRISMA-based bibliometric review of 1,386 relevant publications from the Scopus database (2020-2025) was conducted, using Biblioshiny visualization tools to map key authors, institutions, countries, and emerging research clusters. Findings show a 7.09% annual growth in publications, reflecting a growing global focus on ML, BDA, and cybersecurity within Industry 4.0 ecosystems. The United States, China, and India were identified as major contributors, with strong cross-continental collaborations fostering innovation. Key research topics include deep learning, digital twins, and the Internet of Things (IoT), while emerging areas such as explainable AI, federated analytics, and edge computing are gaining attention. By mapping global research dynamics and identifying key contributors, this study highlights critical research gaps and offers practical insights for advancing interdisciplinary innovation, aimed at creating secure, intelligent, and sustainable industrial ecosystems in Industry 4.0.