Modern cyber-physical systems based on the industrial Internet of Things (IIoT) can be highly distributed and heterogeneous, and that increases the risk of failures due to misbehavior of interconnected components, or other interaction anomalies. In this article, we introduce a conceptual architecture for IIoT anomaly detection based on the paradigms of digital twins (DT) and autonomic computing (AC), and we test it through a proof-of-concept of industrial relevance. The architecture is derived from the current state-of-the-art in DT research and leverages on the MAPE-K feedback loop of AC in order to monitor, analyze, plan, and execute appropriate reconfiguration or mitigation strategies based on the detected deviation from prescriptive behavior stored as shared knowledge. We demonstrate the approach and discuss results by using a reference operational scenario of adequate complexity and criticality within the European Railway Traffic Management System.

Industrial Cyber-Physical Systems (ICPSs) generate cyber and physical data whose joint elaboration can provide insight into ICPSs' operating conditions. Cyber-Physical Anomaly Detection (CPAD) addresses the joint analysis of cyber and physical threats through multi-source and multi-modal data analysis. CPAD is often tailored to specific anomaly types and may use opaque deep learning models, impairing flexibility and explainability. In light of these challenges, we propose a two-level fusion framework for modeling and deploying CPAD in distributed ICPSs. The first detector-level fusion involves deploying CPAD detectors to several distributed ICPS segments and training them through data/decision fusion techniques with historical cyber-physical data. When the distributed ICPS is operational, thus collecting new cyber-physical data, ICPS segments' trained CPAD detectors provide pieces of evidence that go through the second ensemble-level fusion, for which we propose an explainable decision fusion technique based on Time-Varying Dynamic Bayesian networks. The evaluation involves the comprehensive application of the framework to a real hardware-in-the-loop case-study in a laboratory environment. The proposed ensemble-level fusion outperforms the state-of-the-art decision fusion techniques while providing explainable results.

Digital twin development of industrial cyber-physical systems requires modeling, simulation, and monitoring to provide accurate digital replicas mimicking system dynamics. To this aim, modern solutions employ data-driven approaches to capture normal system dynamics using historical data, and anomaly detection using run-time data. However, these approaches are typically process-agnostic and have limited explainability, which negatively impacts trustworthiness. Hence, we propose a novel framework for the digital twin development of industrial cyber-physical systems based on process mining, which connects data-driven and process-based analyses. The framework implements an offline phase that systematically identifies the most suitable process model to capture normal dynamics and simulates faulty dynamics for what-if analysis. The subsequent online phase involves run-time monitoring for anomaly detection. We develop a proof-of-concept application of the framework addressing a water distribution case study that allows physical fault injection. Results highlight the importance of adopting high-quality process models to significantly improve anomaly detection and time performance.

Technological advances in embedded systems and the advent of fog computing led to improved quality of service of applications of cyber-physical systems. In fact, the deployment of such applications on powerful and heterogeneous embedded systems, such as multiprocessors system-on-chips (MPSoCs), allows them to meet latency requirements and real-time operation. Highly relevant to the industry and our reference case-study, the challenging field of nuclear fusion deploys the aforementioned applications, involving high-frequency control with hard real-time and safety constraints. The use of fog computing and MPSoCs is promising to achieve safety, low latency, and timeliness of such control. Indeed, on one hand, applications designed according to fog computing distribute computation across hierarchically organized and geographically distributed edge devices, enabling timely anomaly detection during high-frequency sampling of time series, and, on the other hand, MPSoCs allow leveraging fog computing and integrating monitoring by deploying tasks on a flexible platform suited for mixed-criticality software, leading to so-called mixed criticality systems (MCSs). However, the integration of such software on the same MPSoC opens challenges related to predictability and reliability guarantees, as tasks interfering with each other when accessing the same shared MPSoC resources may introduce non-deterministic latency, possibly leading to failures on account of deadline overruns. Addressing the design, deployment, and evaluation of MCSs on MPSoCs, we propose a model-based system development process that facilitates the integration of real-time and monitoring software on the same platform by means of a formal notation for modeling the design and deployment of MPSoCs. The proposed notation allows developers to leverage embedded hypervisors for monitoring real-time applications and guaranteeing predictability by isolation of hardware resources. Providing evidence of the feasibility of our system development process and evaluating the industry-relevant class of nuclear fusion applications, we experiment with a safety-critical case-study in the context of the ITER nuclear fusion reactor. Our experimentation involves the design and evaluation of several prototypes deployed as MCSs on a virtualized MPSoC, showing that deployment choices linked to the monitor placement and virtualization configurations (e.g., resource allocation, partitioning, and scheduling policies) can significantly impact the predictability of MCSs in terms of Worst-Case Execution Times and other related metrics.