Leveraging Machine Learning for Cybersecurity Resilience in Industry 4.0: Challenges and Future Directions

Industry 4.0, where the convergence of digital technology impacts industrial operations and processes, is characterized by cybersecurity resilience. Therefore, in Industry 4.0, Machine Learning (ML) approaches offer enormous potential for strengthening cyber defenses, guaranteeing resistance against cyber attacks, and improving cyber resilience. One of the most significant trends is the rise of ML in cybersecurity. ML’s ability to analyze vast amounts of data and detect threats that human operators will miss positions it as a crucial tool in the cybersecurity arsenal. This survey offers a comprehensive overview and examines how ML supports facets of cybersecurity, including risk evaluation, incident response sharing threat intelligence, intrusion detection, and safeguarding ML models from attacks. This survey discusses existing techniques’ benefits and drawbacks, identifies emerging trends, and proposes research directions by scrutinizing current frameworks, case studies, and methodologies. Furthermore, we discuss several topics, including predictive risk assessment approaches, collaborative threat intelligence sharing platforms, ML-driven intrusion detection models, automated incident response strategies, and techniques for mitigating manipulations in ML models. Furthermore, the survey identifies the applications of language models in enhancing cybersecurity resilience. This article intends to offer an in-depth look at the advancements by drawing on knowledge from academic disciplines. Moreover, the survey aims to inspire concepts and strategies for bolstering cyber resilience in Industry 4.0 environments.