Customer-individual production in manufacturing is a current trend related to the Industrie 4.0 paradigm. Creation of design files by the customers is becoming more frequent. These design files are typically generated outside the company boundaries and then transferred to the organization where they are eventually processed and scheduled for production. From a security perspective, this introduces new attack vectors targeting producing companies. Design files with malicious configuration parameters can threaten the availability of the manufacturing plant resulting in financial risks and can even cause harm to humans. Human verification of design files is error-prone why an automated solution is required. A graph-theoretic modeling framework for machine tools capable of verifying the security of product designs is proposed. This framework is used to model an exemplary production process implemented in a wood processing plant based on the experiences of a real-world case study. Simulation of the modeled scenario shows the feasibility of the framework. Apart from security verification, the approach can be adopted to decide if a product design can be manufactured with a given set of machine tools.
CCS Concepts• Security and privacy➝Domain-specific security and privacy architectures.
The ongoing transformation of the manufacturing landscape introduces new business opportunities for enterprises but also brings new challenges with it. Especially small-and medium-sized companies (SMEs) require an increasing effort to stay competitive. Data produced on the shop-floor can be harnessed to conduct analyses useful to plant operators, e.g., for optimization of production capabilities or for increasing plant security. Therefore, we propose a privacy-preserving edgecomputing architecture to facilitate a platform for utilizing such applications. Our approach is motivated by requirements from SMEs in Germany, e.g., protection of intellectual property, and employs suitable privacy models. We demonstrate the viability of the proposed framework by evaluation of uses cases for machine chatter optimization and anomaly detection within plants.
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