A Machine Vision Anomaly Detection System to Industry 4.0 Based on Variational Fuzzy Autoencoder

From a technological point of view, Industry 4.0 evolves and operates in a smart environment in which the real and virtual worlds come together through smart cyber-physical systems. These devices that control each other autonomously activate innovative functions that enhance the production process. However, the industrial environment in which the most modern digital automation and information technologies are integrated is an ideal target for large-scale targeted cyberattacks. Implementing an integrated and effective security strategy in the Industrial 4.0 ecosystem presupposes a vertical inspection process at regular intervals to address any new threats and vulnerabilities throughout the production line. This view should be accompanied by the deep conviction of all stakeholders that all systems of modern industrial infrastructure are a potential target of cyberattacks and that the slightest rearrangement of mechatronic systems can lead to generalized losses. Accordingly, given that there is no panacea in designing a security strategy that fully ensures the infrastructure in question, advanced high-level solutions should be adopted, effectively implementing security perimeters without direct dependence on human resources. One of the most important methods of active cybersecurity in Industry 4.0 is the detection of anomalies, i.e., the identification of objects, observations, events, or behaviors that do not conform to the expected pattern of a process. The theme of this work is the identification of defects in the production line resulting from cyberattacks with advanced machine vision methods. An original variational fuzzy autoencoder (VFA) methodology is proposed. Using fuzzy entropy and Euclidean fuzzy similarity measurement maximizes the possibility of using nonlinear transformation through deterministic functions, thus creating an entirely realistic vision system. The final finding is that the proposed system can evaluate and categorize anomalies in a highly complex environment with significant accuracy.