Improving Access Control in Heterogeneous Industrial Big Data Systems

Digital systems integration technologies, digital twins, intelligent control technologies, data-driven methodologies are already widely used in modern industry. Big data is becoming the basis for advanced manufacturing solutions. Distributed heterogeneous (multi-modal) industrial big data systems are appearing in large enterprises. However, the number of attacks on such systems is also increasing day by day. Multi-modal big data systems have special vulnerabilities due to their heterogeneous nature. The most vulnerable is the access control system. Heterogeneous components have different data structuring and security models, which makes it difficult to manage access and develop a unified security policy. In this paper, a new method is advanced to improve the access control system and solve this problem. It is based on a new conceptual data model and distributed audit technology, which were applied earlier. The new method involves finding the optimal unified security policy using multivariate optimization. The final architecture of the access control subsystem includes components of distributed auditing; analysis, security policy design and implementation.