Efficient learning of uncertainty distributions in coupled multidisciplinary systems through sensory data

Most cyber physical systems are composed of subsystems. The subsystems themselves may have smaller sub-systems. Complex cyber physical systems rely heavily on the interplay of dozens of individual sub-systems. Thus, cyber physical systems are typical system of systems (SoS). In order to specify and model such kind of systems, we need develop specification and modeling methods which would be capable to encompass the systems of systems (SoS) specific properties of cyber physical systems. In this paper, we propose a new paradigm for specifying and modeling automotive cyber physical systems based on system-of-systems approach. In this paper, we propose an approach to support specification and modeling automotive cyber physical systems based on systems of systems engineering in the well established modeling language Modelicaml. The main aim of ModelicaML is to enable an efficient and effective way to use Modelica, UML and SysML models reusing notations that are also used for software modeling. We apply formal specification method in requirement analysis process in order to ensure that the software requirements model satisfies required system function and performance goals and constraints, including safety. The effectiveness of the approach is demonstrated with a case study of Vehicular Ad-hoc NETwork.

Reliability evaluation of Markov cyber–physical system oriented to cognition of equipment operating status

Automotive cyber physical systems (CPSs) involve interactions between software controllers, communication networks, and physical devices. These systems are among the most complex cyber physical systems being designed by humans. However, automotive cyber physical systems are not a loose combination of cyber system and physical system, but are a tight and comprehensive integration, and they are ubiquitous spatial-temporal and very large-scale complex systems. In automotive cyber physical systems, the behavior of the physical world such as the velocity, flow and density are dynamic and continuous changing with time while the process of communication and calculation in vehicular cyber system is discrete. In this paper, we extend the AADL to model the cyber world and physical world of automotive cyber physical system, and we propose a method to transform the rule of Cellular Automata to AADL model for modeling spatial-temporal requirements. We also propose an approach to transform the Modelica model to AADL model. The proposed method is illustrated by Vehicular Ad-hoc NETwork (VANET).

In order to learn the performance degradation mode of machines for diagnostics and prognostics in Cyber-Physical System (CPS), it is necessary to analyze observed sensor data to find the internal run-to-failure states of a system. In this paper, the research goal focuses on learning the internal state and transition of the degraded states from original data, which has some advantages to reveal the working dynamics of the system. Due to the existence of unlabeled data, the paper proposes an unsupervised framework based on clustering and Hidden Markov Model, named Cluster-based Hidden Markov Model (cHMM). The cHMM aims at converting raw sensory stream into a sequence of symbols as the initial observation and hidden state sequences, and then an extended Viterbi algorithm based on Hidden Markov Model (HMM) is used to discover the final stable hidden states and transitional rules in a dynamic programming way. Based on the learned model and expert's knowledge, performance degradation failure and the root-cause could be predicted and reasoned. Finally, experiments and proof-of-concept demonstration are given to validate the feasibility and effectiveness of the framework based on C-MAPSS turbofan engine dataset.

Society is increasingly dependent upon the use of distributed cyber-physical systems (CPSs), such as energy networks, chemical processing plants and transport systems. Such CPSs typically have multiple layers of protection to prevent harm to people or the CPS. However, if both the control and protection systems are vulnerable to cyber-attacks, an attack may cause CPS damage or breaches of safety. Such weaknesses in the combined control and protection system are described here as hazardous vulnerabilities (HVs). Providing assurance that a complex CPS has no HVs requires a rigorous process that first identifies potential hazard scenarios and then searches for possible ways that a cyber-attacker could cause them. This article identifies the attributes that a rigorous hazardous vulnerability analysis (HVA) process would require and compares them against related works. None fully meet the requirements for rigour. A solution is proposed, HVA_CPS, which does have the required attributes. HVA_CPS applies a novel combination of two existing analysis techniques: control signal analysis and attack path analysis. The former identifies control actions that lead to hazards, known as hazardous control actions (HCAs); the latter models the system and searches the model for sequences of attack steps that can cause the HCAs. Both analysis techniques have previously been applied alone on different CPSs. The two techniques are integrated by extending the formalism for attack path analysis to capture HCAs. This converts the automated search for attack paths to a selected asset into an exhaustive search for HVs. The integration of the two techniques has been applied using HCAs from an actual CPS. To preserve confidentiality, the application of HVA_CPS is described on a notional electricity generator and its connection to the grid. The value of HVA_CPS is that it delivers rigorous analysis of HVs at system design stage, enabling assurance of their absence throughout the remaining system lifecycle.

A Cyber-physical system (CPS) is an engineering system made of computational components, i.e. cyber elements, and physical elements, that are connected by a communication network. CPSs have emerged as the contemporarily leading technology in major industry sectors such as manufacture, aerospace, automotive, etc. Nowadays CPS is almost the synonym of control systems for large and complex engineering systems. In addition, CPSs have inevitably interweaved with new technologies like Internet of Things, cloud computing, ubiquitous computing, and big data processing. Taming the complexity has been the key challenge in CPS design. A novel declarative computing based platform was proposed in our previous paper to unify modeling and design of both cyber and physical components in CPSs. In this paper, the concepts and principles of the proposed declarative platform are depicted in details. In addition, modeling techniques of declarative networking and declarative control are showcased with concrete simulation examples.

Cyber-physical systems are hybrid networked cyber and engineered physical elements that record data (e.g. using sensors), analyse them using connected services, influence physical processes and interact with human actors using multi-channel interfaces. Examples of CPS interacting with humans in industrial production environments are the so-called cyber-physical production systems (CPPS), where operators supervise the industrial machines, according to the human-in-the-loop paradigm. In this scenario, research challenges for implementing CPPS resilience, promptly reacting to faults, concern: (i) the complex structure of CPPS, which cannot be addressed as a monolithic system, but as a dynamic ecosystem of single CPS interacting and influencing each other; (ii) the volume, velocity and variety of data (Big Data) on which resilience is based, which call for novel methods and techniques to ensure recovery procedures; (iii) the involvement of human factors in these systems. In this paper, we address the design of resilient cyber-physical production systems (R-CPPS) in digital factories by facing these challenges. Specifically, each component of the R-CPPS is modelled as a smart machine, that is, a cyber-physical system equipped with a set of recovery services, a Sensor Data API used to collect sensor data acquired from the physical side for monitoring the component behaviour, and an operator interface for displaying detected anomalous conditions and notifying necessary recovery actions to on-field operators. A context-based mediator, at shop floor level, is in charge of ensuring resilience by gathering data from the CPPS, selecting the proper recovery actions and invoking corresponding recovery services on the target CPS. Finally, data summarisation and relevance evaluation techniques are used for supporting the identification of anomalous conditions in the presence of high volume and velocity of data collected through the Sensor Data API. The approach is validated in a food industry real case study.

The vehicle has been manufactured from a purely physical system based on the laws of mechanics and chemistry, to a more sophisticated Cyber Physical System (CPS) which embeds electronic components, communication components and control systems to improve performance and safety. Therefore, the system in the vehicle or connected vehicle is a typical cyber physical system, which is called the Automotive Cyber-Physical System (ACPS). In this paper; a design methodology for the design of automotive cyber physical systems which include physical world, communication aspect and computation aspect will be presented. An integrated approach to specification and design, analysis of the overall automotive cyber physical system is proposed. This provides a systematic, model based approach to requirements definition, specification and design of automotive cyber physical systems. As automotive cyber physical systems often require real-time capabilities, spatial representation and reasoning, dynamic aspect modeling and physical world modeling the approach presented gives special consideration to these constraints. We give an example for concrete applications of specifying and modeling Vehicular Ad-hoc NETwork, which shows, how the specification, analysis and design of automotive cyber physical systems are supported by the proposed approach.

Cyber-physical systems (CPS) play a pivotal role in various critical applications, ranging from industrial automation to healthcare monitoring. Ensuring the reliability and accuracy of sensor data within these systems is of paramount importance. This research paper presents a novel approach for enhancing fault detection in sensor data within a cyber-physical system through the integration of machine learning algorithms. Specifically, a hybrid ensemble methodology is proposed, combining the strengths of AdaBoost and Random Forest with Rocchio’s algorithm, to achieve robust and accurate fault detection. The proposed approach operates in two phases. In the first phase, AdaBoost and Random Forest classifiers are trained on a diverse dataset containing normal and faulty sensor data to develop individual base models. AdaBoost emphasizes misclassified instances, while Random Forest focuses on capturing complex interactions within the data. In the second phase, the outputs of these base models are fused using Rocchio’s algorithm, which exploits the similarities between faulty instances to improve fault detection accuracy. Comparative analyses are conducted against individual classifiers and other ensemble methods to validate the effectiveness of the hybrid approach. The results demonstrate that the proposed approach achieves superior fault detection rates. Additionally, the integration of Rocchio’s algorithm significantly contributes to the refinement of the fault detection process, effectively leveraging the strengths of AdaBoost and Random Forest. In conclusion, this research offers a comprehensive solution to enhance fault detection capabilities in cyber-physical systems by introducing a novel ensemble framework. By synergistically combining AdaBoost, Random Forest, and Rocchio’s algorithm, the proposed methodology provides a robust mechanism for accurately identifying sensor data anomalies, thus bolstering the reliability and performance of cyber-physical systems across a multitude of critical applications.

Fed-MPS: Federated learning with local differential privacy using model parameter selection for resource-constrained CPS

Cyber-Physical Systems (CPS) combine software with the physical world. For this purpose, CPS must model physical behavior in software. However, a software-based model cannot always accurately reflect the physical world. Often the model is a simplification of complex physical processes, or it suffers from measurement errors, or the physical side is subject to modifications and parameter drift, or the model is simply subject to misconceptions. It is an open research challenge how we can verify that physics and software-based model fit together. However, to rely on CPS in real-world scenarios we must ensure that physics and model are aligned. We propose a model formalism based on hidden Markov models that considers uncertainty and unknown phenomena and is robust enough to allow the analysis of CPS when working with error-prone data. More specifically, given observation data and an instance of the proposed model for a CPS (both of which may be flawed) the proposed formalism allows us to quantify the suitability between physics and model. If, however, a given model instance is deemed correct, the formalism enables methods which identify and smooth corrupt observation data as well as compute the most likely sequence of events for a given set of observations. Additionally, the formalism enables the learning of a suitable model according to given observation data. The model formalism will be tested with a simulation and a case study of an overhead traveling cargo crane system.

A speech recognizer based on a hidden Markov model (HMM) representation of quantized articulatory features is described, and experimental results for its evaluation are presented. Traditional schemes for HMM representation of speech have attempted to model a set of disjoint time segments. In order to create a more robust speech recognition system, the speech production system is characterized by a set of articulatory features, each of which are allowed to vary over a range of discrete values. Target configurations of articulators are represented by sets of feature values. These feature values are permitted to vary independently and asynchronously (with appropriate constraints) as the production system moves from one target configuration to the next. This avoids the abrupt model changes inherent in non-overlapping segment modeling. The feature value combinations that occur while in transit between target configurations represent the coarticulation intervals between the two targets. This scheme is implemented using an ergodic HMM to control the evolution of the feature values as the system moves from one target configuration to the next. Speech recognition results show that the new system consistently outperforms the traditional HMM approaches.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

The industrial Internet of things (IIoT) can be regarded as machines, computers, and people enabling intelligent industrial operations using advanced data analytics. It is a network of systems, objects, platforms, and applications that can communicate and share intelligence. This chapter introduces the concept of IIoT along with its security challenges and applications. It covers in detail about the existing security issues in the area of cyber physical systems (CPS) and evolution of IIoT with a secure design pattern. IIoT is an amalgamation of various technologies, like big data, machine learning, machine-to-machine communication, sensor data, and automation that have been involved in the industrial backdrop for past years. CPS was developed from the massive application of embedded systems. CPS is considered as the core part and foundation of Industry 4.0. CPS provides maximum benefits and will exchange industrial operations. CPS will focus on technologies, concept, challenges, and architecture. IIoT becomes more manageable if all the software, policy, and updates are up to date. The deployed version of the automation system should be carefully controlled, managed, and configured. Periodic compliance report about security is advisable and mandatory.

Cyber-physical systems (CPS) are vulnerable to network attacks because communication relies on the network that links the various components in the CPS. The importance of network security is self-evident. In this study, we conduct a network security risk assessment from the perspectives of the host and the network, and we propose a new framework for a multidimensional network security risk assessment that includes two stages, i.e., risk identification and risk calculation. For the risk identification stage, we propose a multidimensional hierarchical index system for assessing cybersecurity risk; the system's security status is determined in three dimensions, i.e., basic operation, vulnerabilities, and threats, and these dimensions guide the data collection. In the risk calculation stage, we use a hidden Markov model (HMM) to assess the network security risk. We provide a new definition of the quality of alert and optimize the observation sequence of the HMM. The model uses a learning algorithm instead of setting the parameters manually. We introduce the concept of network node association to increase the reliability and accuracy of the risk assessment. The simulation results show that the proposed index system provides quantitative data that reflect the security status of the network. The proposed network security risk assessment method based on the improved HMM (I-HMM) reflects the security risk status in a timely and intuitive manner and detects the degree of risk that different hosts pose to the network.

Cyber Physical System (CPS) is a complex interdisciplinary engineering system with amalgamation of physical-realm entities like machines, sensors, actuators and embedded devices with the cyber-realm system constituting of communication networks, Internet, and network-centric heterogeneous computing platforms like cloud and Fog/Edge computing. Further, with the recent advancements in the field of Internet of Things (IoT) and Machine-to-Machine (M2M) communication as enabling technologies; it is possible to design large scale CPS and deployment of application-specific sensordata acquisition and control systems. This has unfolded another technological dimension of huge data-centric subsystems: Big-Data and Artificial Intelligence (AI) and Machine Learning (ML) based application specific data analytics requirement for future-ready intelligent CPS also popularly referred as Cognitive CPS (CCPS). In this paper, we have proposed a novel four-layer architecture and their design framework with the vision of Next-generation Cyber Physical System (NG-CPS). Some major design attributes of each layer have been considered to formulate eight NG-CPS design goals with a modelling approach and suggested some major design aspects including modularity, scalability.