Securing Industry 4.0 with a Deep Learning Driven Framework for Advanced Data Privacy Detection
Aqeel Ali Al-Hilali, Jasim Mohammed Rajaa, Mariam J. Manee, Salah Farhan A Sharif, Marwan Aziz Mohammed · 2025
Industry 4.0 tools offer full-stack control and growth of supply chains and manufacturing processes. This surge raises additional security risks for Industry 4.0, although this is mainly to the emergence of sophisticated cyberattacks. To highlight the roles that Industry 4.0's major enabling components play in protecting the manufacturing system's product lifecycle, this paper presents a deep learning-based privacy detection framework for Industrial 4.0 employing IoT sensors (DL-PDF-IoT). An evaluation system is immediately protected from threats using IoT gateways thanks to the study that creates sophisticated filters. Using network data, each filter creates a deep learning model to detect cyberattacks. The suggested method considerably decreases data leakage and network traffic while improving detection accuracy during data transmission between IoT gateways. By teaching IoT nodes this information, they will be better able to detect breaches. According to the study, the proposed methodology outperforms other methods of preserving users' personal information during planned maintenance sessions at each level. Compared to prior approaches, the proposed DL-PDF-IoT mechanism has a high privacy ratio of 98.3%, an attack prevention ratio of 97.4%, and a low energy consumption ratio of 25.7%.