Biblio

In Production System Engineering (PSE), domain experts from different disciplines reuse assets such as products, production processes, and resources. Therefore, PSE organizations aim at establishing reuse across engineering disciplines. However, the coordination of multi-disciplinary reuse tasks, e.g., the re-validation of related assets after changes, is hampered by the coarse-grained representation of tasks and by scattered, heterogeneous domain knowledge. This paper introduces the Multi-disciplinary Reuse Coordination (MRC) artifact to improve task management for multi-disciplinary reuse. For assets and their properties, the MRC artifact describes sub-tasks with progress and result states to provide references for detailed reuse task management across engineering disciplines. In a feasibility study on a typical robot cell in automotive manufacturing, we investigate the effectiveness of task management with the MRC artifact compared to traditional approaches. Results indicate that the MRC artifact is feasible and provides effective capabilities for coordinating multi-disciplinary re-validation after changes.

The fourth industrial revolution has led to the rapid development of industrial control systems. While the large number of industrial system devices connected to the Internet provides convenience for production management, it also exposes industrial control systems to more attack surfaces. Under the influence of multiple attack surfaces, sensitive data leakage has a more serious and time-spanning negative impact on industrial production systems. How to quickly locate the source of information leakage plays a crucial role in reducing the loss from the attack, so there are new requirements for tracing sensitive data in industrial control information systems. In this paper, we propose a digital watermarking traceability scheme for sensitive data in industrial control systems to address the above problems. In this scheme, we enhance the granularity of traceability by classifying sensitive data types of industrial control systems into text, image and video data with differentiated processing, and achieve accurate positioning of data sources by combining technologies such as national secret asymmetric encryption and hash message authentication codes, and mitigate the impact of mainstream watermarking technologies such as obfuscation attacks and copy attacks on sensitive data. It also mitigates the attacks against the watermarking traceability such as obfuscation attacks and copy attacks. At the same time, this scheme designs a data flow watermark monitoring module on the post-node of the data source to monitor the unauthorized sensitive data access behavior caused by other attacks.

In Production System Engineering (PSE), domain experts aim at effectively and efficiently analyzing and mitigating information security risks to product and process qualities for manufacturing. However, traditional security standards do not connect security analysis to the value stream of the production system nor to production quality requirements. This paper aims at facilitating security analysis for production quality already in the design phase of PSE. In this paper, we (i) identify the connection between security and production quality, and (ii) introduce the Production Security Network (PSN) to efficiently derive reusable security requirements and design patterns for PSE. We evaluate the PSN with threat scenarios in a feasibility study. The study results indicate that the PSN satisfies the requirements for systematic security analysis. The design patterns provide a good foundation for improving the communication of domain experts by connecting security and quality concerns.

Machine learning (ML) has been applied in prognostics and health management (PHM) to monitor and predict the health of industrial machinery. The use of PHM in production systems creates a cyber-physical, omni-layer system. While ML offers statistical improvements over previous methods, and brings statistical models to bear on new systems and PHM tasks, it is susceptible to performance degradation when the behavior of the systems that ML is receiving its inputs from changes. Natural changes such as physical wear and engineered changes such as maintenance and rebuild procedures are catalysts for performance degradation, and are both inherent to production systems. Drawing from data on the impact of maintenance procedures on ML performance in hydraulic actuators, this paper presents a simulation study that investigates how long it takes for ML performance degradation to create a difference in the throughput of serial production system. In particular, this investigation considers the performance of an ML model learned on data collected before a rebuild procedure is conducted on a hydraulic actuator and an ML model transfer learned on data collected after the rebuild procedure. Transfer learning is able to mitigate performance degradation, but there is still a significant impact on throughput. The conclusion is drawn that ML faults can have drastic, non-linear effects on the throughput of production systems.

Technical debt is an analogy introduced in 1992 by Cunningham to help explain how intentional decisions not to follow a gold standard or best practice in order to save time or effort during creation of software can later on lead to a product of lower quality in terms of product quality itself, reliability, maintainability or extensibility. Little work has been done so far that applies this analogy to cyber physical (production) systems (CP(P)S). Also there is only little work that uses this analogy for security related issues. This work aims to fill this gap: We want to find out which security related symptoms within the field of cyber physical production systems can be traced back to TD items during all phases, from requirements and design down to maintenance and operation. This work shall support experts from the field by being a first step in exploring the relationship between not following security best practices and concrete increase of costs due to TD as consequence.

As the connectivity within manufacturing processes increases in light of Industry 4.0, information security becomes a pressing issue for product suppliers, systems integrators, and asset owners. Reaching new heights in digitizing the manufacturing industry also provides more targets for cyber attacks, hence, cyber-physical production systems (CPPSs) must be adequately secured to prevent malicious acts. To achieve a sufficient level of security, proper defense mechanisms must be integrated already early on in the systems' lifecycle and not just eventually in the operation phase. Although standardization efforts exist with the objective of guiding involved stakeholders toward the establishment of a holistic industrial security concept (e.g., IEC 62443), a dedicated security development lifecycle for systems integrators is missing. This represents a major challenge for engineers who lack sufficient information security knowledge, as they may not be able to identify security-related activities that can be performed along the production systems engineering (PSE) process. In this paper, we propose a novel methodology named Security Development Lifecycle for Cyber-Physical Production Systems (SDL-CPPS) that aims to foster security by design for CPPSs, i.e., the engineering of smart production systems with security in mind. More specifically, we derive security-related activities based on (i) security standards and guidelines, and (ii) relevant literature, leading to a security-improved PSE process that can be implemented by systems integrators. Furthermore, this paper informs domain experts on how they can conduct these security-enhancing activities and provides pointers to relevant works that may fill the potential knowledge gap. Finally, we review the proposed approach by means of discussions in a workshop setting with technical managers of an Austrian-based systems integrator to identify barriers to adopting the SDL-CPPS.

Responding to network security incidents requires interference with ongoing attacks to restore the security of services running on production systems. This approach prevents damage, but drastically impedes the collection of threat intelligence and the analysis of vulnerabilities, exploits, and attack strategies. We propose the live confinement of suspicious microservices into a sandbox network that allows to monitor and analyze ongoing attacks under quarantine and that retains an image of the vulnerable and open production network. A successful sandboxing requires that it happens completely transparent to and cannot be detected by an attacker. Therefore, we introduce a novel metric to measure the Quality of Deception (QoD) and use it to evaluate three proposed network deception mechanisms. Our evaluation results indicate that in our evaluation scenario in best case, an optimal QoD is achieved. In worst case, only a small downtime of approx. 3s per microservice (MS) occurs and thus a momentary drop in QoD to 70.26% before it converges back to optimum as the quarantined services are restored.

In a diagnosability verifier with polynomial complexity, a non-diagnosable system generates uncertain loops. Such forbidden loops are in this paper transformed to forbidden states by simple detector automata. The forbidden state problem is trivially transformed to a nonblocking problem by considering all states except the forbidden ones as marked states. This transformation is combined with one of the most efficient abstractions for modular systems called conflict equivalence, where nonblocking properties are preserved. In the resulting abstraction, local events are hidden and more local events are achieved when subsystems are synchronized. This incremental abstraction is applied to a scalable production system, including parallel lines where buffers and machines in each line include some typical failures and feedback flows. For this modular system, the proposed diagnosability algorithm shows great results, where diagnosability of systems including millions of states is analyzed in less than a second.

Our previous work, which can be referred to as EMPRESS 1.0, showed that rich metadata management provides a relatively low-overhead approach to facilitating insight from scale-up scientific applications. However, this system did not provide the functionality needed for a viable production system or address whether such a system could scale. Therefore, we have extended our previous work to create EMPRESS 2.0, which incorporates the features required for a useful production system. Through a discussion of EMPRESS 2.0, this paper explores how to incorporate rich query functionality, fault tolerance, and atomic operations into a scalable, storage system independent metadata management system that is easy to use. This paper demonstrates that such a system offers significant performance advantages over HDF5, providing metadata querying that is 150X to 650X faster, and can greatly accelerate post-processing. Finally, since the current implementation of EMPRESS 2.0 relies on an RDBMS, this paper demonstrates that an RDBMS is a viable technology for managing data-oriented metadata.