Vulnerabilities and safety assurance methods in Cyber-Physical Systems: A comprehensive review

The pervasiveness and growing complexity of software systems are challenging software engineering to design systems that can adapt their behavior to withstand unpredictable, uncertain, and continuously changing execution environments. Control theoretical adaptation mechanisms have received growing interest from the software engineering community in the last few years for their mathematical grounding, allowing formal guarantees on the behavior of the controlled systems. However, most of these mechanisms are tailored to specific applications and can hardly be generalized into broadly applicable software design and development processes.This article discusses a reference control design process, from goal identification to the verification and validation of the controlled system. A taxonomy of the main control strategies is introduced, analyzing their applicability to software adaptation for both functional and nonfunctional goals. A brief extract on how to deal with uncertainty complements the discussion. Finally, the article highlights a set of open challenges, both for the software engineering and the control theory research communities.

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Control Strategies for Self-Adaptive Software Systems

Current concept, development, and testing applications in production concerning Cyber-Physical Systems (CPS), Industry 4.0 (I40), and Internet of Things (IoT) are mainly addressing fully autonomous systems, fostered by an increase in available technologies regarding distributed decision-making, sensors, and actuators for robotics systems. This is applied also to production logistics settings with a multitude of transport tasks, e.g., between warehousing or material supply stations and production locations within larger production sites as for example in the automotive industry. In most cases, mixed environments where automated systems and humans collaborate (e.g., cobots) are not in the center of analysis and development endeavors although the worker’s adoption and acceptance of new technologies are of crucial relevance. From an interdisciplinary research perspective, this constitutes an important research gap, as the future challenges for successful automated systems will rely mainly on human-computer interaction (HCI) in connection with an efficient collaboration between motivated workers, automated robotics, and transportation systems. We develop a HCI efficiency description in production logistics based on an interdisciplinary analysis consisting of three interdependent parts: (i) a production logistics literature review and process study, (ii) a computer science literature review and simulation study for an existing autonomous traffic control algorithm applicable to production logistics settings with the specific inclusion of human actors, and (iii) a work science analysis for automation settings referring to theoretical foundations and empirical findings regarding the management of workers in digital work settings. We conclude with practical implications and discuss avenues for future research and business applications.

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Production logistics and human-computer interaction—state-of-the-art, challenges and requirements for the future

ContextCyber-physical systems (CPSs) have emerged to be the next generation of engineered systems driving the so-called fourth industrial revolution. CPSs are becoming more complex, open and more prone to security threats, which urges security to be engineered systematically into CPSs. Model-Based Security Engineering (MBSE) could be a key means to tackle this challenge via security by design, abstraction, and automation. ObjectiveWe aim at providing an initial assessment of the state of the art in MBSE for CPSs (MBSE4CPS). Specifically, this work focuses on finding out 1) the publication statistics of MBSE4CPS studies; 2) the characteristics of MBSE4CPS studies; and 3) the open issues of MBSE4CPS research. MethodWe conducted a systematic mapping study (SMS) following a rigorous protocol that was developed based on the state-of-the-art SMS and systematic review guidelines. From thousands of relevant publications, we systematically identified 48 primary MBSE4CPS studies for data extraction and synthesis to answer predefined research questions. ResultsSMS results show that for three recent years (2014-2016) the number of primary MBSE4CPS studies has increased significantly. Within the primary studies, the popularity of using Domain-Specific Languages (DSLs) is comparable with the use of the standardised UML modelling notation. Most primary studies do not explicitly address specific security concerns (e.g., confidentiality, integrity) but rather focus on security analyses in general on threats, attacks or vulnerabilities. Few primary studies propose to engineer security solutions for CPSs. Many focus on the early stages of development lifecycle such as security requirement engineering or analysis. ConclusionThe SMS does not only provide the state of the art in MBSE4CPS, but also points out several open issues that would deserve more investigation, e.g., the lack of engineering security solutions for CPSs, limited tool support, too few industrial case studies, and the challenge of bridging DSLs in engineering secure CPSs.

Model-based security engineering for cyber-physical systems

It is important that a Cyber-Physical System (CPS) with uncertainty in its behavior caused by its unpredictable operating environment, to ensure its reliable operation. One method to ensure that the CPS will handle such uncertainty during its operation is by testing the CPS with model-based testing (MBT) techniques. However, existing MBT techniques do not explicitly capture uncertainty in test ready models, i.e., capturing the uncertain expected behavior of a CPS in the presence of environment uncertainty. To fill this gap, we present an Uncertainty-Wise test-modeling framework, named as UncerTum, to create test ready models to support MBT of CPSs facing uncertainty. UncerTum relies on the definition of a UML profile [the UML Uncertainty Profile (UUP)] and a set of UML Model Libraries extending the UML profile for Modeling and Analysis of Real-Time and Embedded Systems (MARTE). UncerTum also benefits from the UML Testing Profile V.2 to support standard-based MBT. UncerTum was evaluated with two industrial CPS case studies, one real-world case study, and one open-source CPS case study from the following four perspectives: (1) Completeness and Coverage of the profiles and Model Libraries in terms of concepts defined in their underlying uncertainty conceptual model for CPSs, i.e., U-Model and MARTE, (2) Effort required to model uncertainty with UncerTum, and (3) Correctness of the developed test ready models, which was assessed via model execution. Based on the evaluation, we can conclude that we were successful in modeling all the uncertainties identified in the four case studies, which gives us an indication that UncerTum is sufficiently complete. In terms of modeling effort, we concluded that on average UncerTum requires 18.5% more time to apply stereotypes from UUP on test ready models.

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Uncertainty-Wise Cyber-Physical System test modeling

Uncertainty is intrinsic in most technical systems, including Cyber-Physical Systems CPS. Therefore, handling uncertainty in a graceful manner during the real operation of CPS is critical. Since designing, developing, and testing modern and highly sophisticated CPS is an expanding field, a step towards dealing with uncertainty is to identify, define, and classify uncertainties at various levels of CPS. This will help develop a systematic and comprehensive understanding of uncertainty. To that end, we propose a conceptual model for uncertainty specifically designed for CPS. Since the study of uncertainty in CPS development and testing is still irrelatively unexplored, this conceptual model was derived in a large part by reviewing existing work on uncertainty in other fields, including philosophy, physics, statistics, and healthcare. The conceptual model is mapped to the three logical levels of CPS: Application, Infrastructure, and Integration. It is captured using UML class diagrams, including relevant OCL constraints. To validate the conceptual model, we identified, classified, and specified uncertainties in two distinct industrial case studies.

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Roland Norgren

Papers

Understanding Uncertainty in Cyber-Physical Systems: A Conceptual Model

Uncertainty-Wise Cyber-Physical System test modeling