Modeling Of Cyber-physical Systems Research Articles

A Framework for the Optimization of Complex Cyber-Physical Systems via Directed Acyclic Graph.

Mathematical modeling and data-driven methodologies are frequently required to optimize industrial processes in the context of Cyber-Physical Systems (CPS). This paper introduces the PipeGraph software library, an open-source python toolbox for easing the creation of machine learning models by using Directed Acyclic Graph (DAG)-like implementations that can be used for CPS. scikit-learn’s Pipeline is a very useful tool to bind a sequence of transformers and a final estimator in a single unit capable of working itself as an estimator. It sequentially assembles several steps that can be cross-validated together while setting different parameters. Steps encapsulation secures the experiment from data leakage during the training phase. The scientific goal of PipeGraph is to extend the concept of Pipeline by using a graph structure that can handle scikit-learn’s objects in DAG layouts. It allows performing diverse operations, instead of only transformations, following the topological ordering of the steps in the graph; it provides access to all the data generated along the intermediate steps; and it is compatible with GridSearchCV function to tune the hyperparameters of the steps. It is also not limited to entries. Moreover, it has been proposed as part of the scikit-learn-contrib supported project, and is fully compatible with scikit-learn. Documentation and unitary tests are publicly available together with the source code. Two case studies are analyzed in which PipeGraph proves to be essential in improving CPS modeling and optimization: the first is about the optimization of a heat exchange management system, and the second deals with the detection of anomalies in manufacturing processes.

Cyber–physical system modeling with Modelica using message passing communication

Modelica is an object-oriented modeling language whose design and features facilitate the description of cyber–physical systems (CPS). Message passing communication (MPC), seen as the transmission of impulses of information between model components, eases the description of the discrete-event parts of CPS models. However, Modelica does not currently supports MPC. Modelica supports an equation-based component connection rationale, where Modelica tools automatically transform component connections into model equations, following a physical modeling approach. The differences between MPC and Modelica connections are analyzed. A proposal for supporting MPC in Modelica is presented, inspired by the coupled PDEVS model communication approach. The presented MPC proposal is based on the definition of structures to manage messages, named buffers, interface ports and communication channels. Also, an implementation of the proposed MPC mechanism in the form of a new free Modelica library, named MSGLib, is presented. MSGLib includes functionality to manage and dynamically store messages, and describe component communications. Two examples, a pick and place system and a robotic arm, are presented to demonstrate the use of the library, and its combination with other Modelica models.

Threat Modelling of Cyber Physical Systems: A Real Case Study Based on Window Cleaning Business

Threat modelling Cyber-Physical System built on cloud infrastructure to monitor and manage the window cleaning operation using Window Cleaning Warehouse as a case study. Focusing on IoT data collection and cloud infrastructure security and the connections with the Cyber-Physical System. External dependencies and trust levels are defined before using trust boundaries and data flow diagrams to highlight attack surfaces. Expected scenarios from the data flow diagrams are discussed to identify violated intended use of the system using STRIDE threat classification. A risk assessment of assets that may be of interest to an adversary aid the discovery of more security risks that are then prioritised using the DREAD methodology. The results of the research present a comprehensive breakdown of vulnerabilities associated with IoT data security for route optimisation ranging from GPS spoofing, to Firestore vulnerabilities in the real-time database to Bluetooth Low Energy vulnerabilities in the IoT hardware, all of which could be common risks in cyber-physical systems designed by SME businesses. The research concludes various security risks applicable to SME businesses adopting industry 4.0 to alleviate the risk of new security breaches to the business through this adoption, increasing the likelihood of successful adoption of industry 4.0.

Optimal Machine Learning Enabled Intrusion Detection in Cyber-Physical System Environment

Cyber-attacks on cyber-physical systems (CPSs) resulted to sensing and actuation misbehavior, severe damage to physical object, and safety risk. Machine learning (ML) models have been presented to hinder cyberattacks on the CPS environment; however, the non-existence of labelled data from new attacks makes their detection quite interesting. Intrusion Detection System (IDS) is a commonly utilized to detect and classify the existence of intrusions in the CPS environment, which acts as an important part in secure CPS environment. Latest developments in deep learning (DL) and explainable artificial intelligence (XAI) stimulate new IDSs to manage cyberattacks with minimum complexity and high sophistication. In this aspect, this paper presents an XAI based IDS using feature selection with Dirichlet Variational Autoencoder (XAIIDS-FSDVAE) model for CPS. The proposed model encompasses the design of coyote optimization algorithm (COA) based feature selection (FS) model is derived to select an optimal subset of features. Next, an intelligent Dirichlet Variational Autoencoder (DVAE) technique is employed for the anomaly detection process in the CPS environment. Finally, the parameter optimization of the DVAE takes place using a manta ray foraging optimization (MRFO) model to tune the parameter of the DVAE. In order to determine the enhanced intrusion detection efficiency of the XAIIDS-FSDVAE technique, a wide range of simulations take place using the benchmark datasets. The experimental results reported the better performance of the XAIIDS-FSDVAE technique over the recent methods in terms of several evaluation parameters.

Deep Learning with Image Classification Based Secure CPS for Healthcare Sector

Cyber-Physical System (CPS) involves the combination of physical processes with computation and communication systems. The recent advancements made in cloud computing, Wireless Sensor Network (WSN), healthcare sensors, etc. tend to develop CPS as a proficient model for healthcare applications especially, home patient care. Though several techniques have been proposed earlier related to CPS structures, only a handful of studies has focused on the design of CPS models for health care sector. So, the proposal for a dedicated CPS model for healthcare sector necessitates a significant interest to ensure data privacy. To overcome the challenges, the current research paper designs a Deep Learning-based Intrusion Detection and Image Classification for Secure CPS (DLIDIC-SCPS) model for healthcare sector. The aim of the proposed DLIDIC-SCPS model is to achieve secure image transmission and image classification process for CPS in healthcare sector. Primarily, data acquisition takes place with the help of sensors and detection of intrusions is performed using Fuzzy Deep Neural Network (FDNN) technique. Besides, Multiple Share Creation (MSC) approach is used to create several shares of medical image so as to accomplish security. Also, blockchain is employed as a distributed data storage entity to create a ledger that provides access to the client. For image classification, Inception v3 with Fuzzy Wavelet Neural Network (FWNN) is utilized that diagnose the disease from the applied medical image. Finally, Salp Swarm Algorithm (SSA) is utilized to fine tune the parameters involved in WNN model, thereby boosting its classification performance. A wide range of simulations was carried out to highlight the superiority of the proposed DLIDIC-SCPS technique. The simulation outcomes confirm that DLIDIC-SCPS approach demonstrates promising results in terms of security, privacy, and image classification outcomes over recent state-of-the-art techniques.

A Monadic Co-simulation Model for Cyber-physical Production Systems

The production flexibility required by the new industrial revolutions is largely based on heterogeneous Cyber-Physical Production Systems models that cooperate with each other to perform complex tasks. To accomplish tasks at an acceptable pace, CPPSs should be based on appropriate cooperation mechanisms. To this end a CPPS must be able to provide services in the form of functionalities to other CPPSs, and also to use functionalities of other CPPSs. The cooperation of two CPPS systems is done by co-simulating the two models that allow the partial or total access of the functionalities of one system, by the other system. Requests from one CPPS to another CPPS create connection moments of the two models that can only be performed in certain states of the two models. Also, the answers to these requests create connections between the two models in other subsequent states. Optimal aggregation of the behaviors of the two models, by co-simulation, is essential because otherwise it can lead to very long waiting times and can cause major problems if not done correctly. We will see in this paper that the behavior of such a simulation model can be represented by a category, and the co-simulation of two models can be defined by a monad determined by two adjoint functors between the simulation categories of the two models.

Modular data model for energy-flexible cyber-physical production systems

Due to the massive expansion of renewable energies and the growing importance of carbon pricing, energy prices are rising, and industrial companies must put higher effort in purchasing electrical energy cost-efficiently. Additionally, the increasing share of renewable energies leads to more volatility in the power market, giving companies the opportunity to reduce their energy costs by using energy-flexibility measures. As thermal supply systems offer a great potential for energy flexibility, building automation systems which control the supply systems must be adapted for communication and external access. Intelligent algorithms interacting with the automation can enable factories to fexibilize their energy purchases and to participate in energy markets by using external information, such as energy prices or forecasts of the machine’s energy demand. This requires seamless communication that connects all hierarchy levels of the Reference Architectural Model Industry 4.0 (RAMI 4.0) from connected world to field level. Also functional safety must be ensured. Moreover, energy-flexibility measures must be validated within simulation environments which should show the same data structure. Therefore, a standardized data model that can be applied across different companies is necessary. We propose a concept for a modular and consistent data model for automation, simulation, and communication to enable simple data exchange using external algorithms. Therefore, base classes containing variables for generic actors and sensors are developed and extended by further variables to model systems based on their control mode. Moreover, a safety module to manage external access by algorithms is included. With this data model, industrial energy supply systems can be easily expanded to include additional components. Furthermore, energy supply systems consisting of several components, such as energy converters, pumps and valves can be modeled in an simple and time-efficient way. The data model is applied to a compression chiller at ETA Research Factory.

A Survey of Cyber-Physical Power System Modeling Methods for Future Energy Systems

The grid of the future will have a higher penetration of grid edge devices that enable increased automation and grid edge intelligence. The current grid models do not account for these grid-edge devices, and the creation of cyber-physical models for the grid is essential to understand the impact of these devices. Although existing cyber-physical power system (CPPS) models have been developed using a wide variety of approaches, a comprehensive review of the validity of these approaches and their suitability for modeling the future grid has not been performed. In a CPPS, the physical layer usually consists of the power grid and protection devices, whereas the cyber layer consists of communication, computation, and control components. This paper provides a review on the existing approaches to model CPPS and to characterize the inter- and intra-actions for distributed autonomous systems. The CPPS models can then be used to perform various analyses, such as cyberattack analysis, threat analysis, and resilience analysis. A qualitative evaluation criteria for the various modeling paradigm is discussed to help researchers understand the trade-offs in choosing the right modeling method for their particular application.

Text mining techniques for the management of predictive maintenance

The advent of Industry 4.0 provides new opportunities to improve the maintenance of production equipment from both the technical and managerial perspective. In this paper, we propose a contribution in the direction of predictive maintenance of machine tools based on the integration of a text mining algorithm with the cyber-physical system of a manufacturing industry. The system performs its analysis starting from data stored in log files maintained by a machine tool returning an alert about a future potential machine failure. Log files, produced by part programs running on the machine control system, record the status of execution parameters taken by key sensors or derived by the control system during the part program execution. Historical data are collected by means of Digital Twin technologies and then analyzed using computational linguistic techniques so that we can predict a machine failure in the imminent future starting from data collected in the past. The paper first describes a new scheme for the classification of maintenance approaches. Then, starting from the proposed cyber-physical system model, an algorithm for predictive maintenance based on text mining technology is integrated in it. The implemented tool supports the maintenance manager in making the most appropriate decisions about the scheduling of maintenance activities when there is an alert about a possible machine failure.

Benefits of Cyber-Physical Systems Modeling and Simulation with LabView

Cyber-physical systems (CPSs) as part of the Industry 4.0 strategy, represents one of the most challenging research topic for engineers. It’s well known that CPSs integrate at highest level digital technology (computation, control and networking) into physical objects. However, by incorporating various heterogeneous subsystems with different energy levels and functionalities, both analogue and digital signals management, as well as a large scale of communication and information technologies, their modeling and simulation becomes a difficult engineering task. In the past years a huge number of research papers has been dedicated to identify and develop modeling techniques and simulation toolkits being able to handle CPSs complexity. Following these trends, this paper is focused to research and evidence issues linked with the LabView graphically oriented programming technology utilization for CPSs modelling and simulation purposes. Both advantages and shortcomings of this very special technology are studied in order to design and implement a viable software framework being able to model and simulate various CPS structures. As a concrete example, a specific CPS configuration consist of six computer-based mechatronic systems that constitute a laboratory-prototype manufacturing plant has been chosen for LabView-based modeling and simulation. Various virtual instrument-type models of this setup has been conceived and proposed for real-time simulation. It has been concluded then that the application of LabView technology lead to interesting and useful results. The paper specially highlights the benefits and versatility of LabView utilization for complex CPSs modeling and simulation purposes.

Sensors in Cyber-Physical Systems Based on Android Operating System

The cyber-physical systems take the major part of any system that help users to interact with environment processes. Cyber-physical systems are intelligent systems, which include networks of physical and computing components that interact on internal level. The basis for the development of various models of cyber-physical systems are the using of measuring instruments and their software. Measuring instruments are necessary to control technological parameters processes and the environment. The purpose was to investigate the features of interaction with sensors, to identify the most useful of them in use, to classify types and describe their capabilities for future use in developing of cyber-physical systems. The relevance of the choice of this topic is that mobile and cyber-physical systems occupy a significant place in modern life. The systems that help the user to simplify daily tasks are of maximum benefit. These tasks can be attributed to the tasks of the environment as they exist and are performed in it. Especially cyber-physical systems that interact with the environment have the ability to solve such problems. Sensors act as a tool of interaction, the so-called bridge between the environment and the program. Sensors collect and provide information for further processing and use in solving problems.

Compositional Thinking in Cyberphysical Systems Theory

Engineering safe and secure cyber-physical systems requires system engineers to develop and maintain a number of model views, both dynamic and static, which can be seen as algebras. We posit that verifying the composition of requirement, behavioral, and architectural models using category theory gives rise to a strictly compositional interpretation of cyber-physical systems theory, which can assist in the modeling and analysis of safety-critical cyber-physical systems.

Time in Cyber-Physical Systems Specifications, Modeling and Measurements

This paper addresses the role, interpretation and the deployment of the notion “time” in distributed cyber-physical systems. It discusses various possibilities how to approach such modeling and selects the fitting one, which enables to utilize the related specification language ASL in the domain applications.

Development of a method for assessing the security of cyber-physical systems based on the Lotka–Volterra model

The paper presents the results of the development of a method for assessing the security of cyber-physical systems based on the Lotka–Volterra model. Security models of cyber-physical systems are proposed: “predator–prey” taking into account the computing capabilities and focus of targeted cyberattacks, “predator–prey” taking into account the possible competition of attackers in relation to the “prey”, “predator–prey” taking into account the relationships between “prey species” and “predator species”, “predator–prey” taking into account the relationship between “prey species” and “predator species”. Based on the proposed approach, the coefficients of the Lotka–Volterra model α=0.39, β=0.32, γ=0.29, φ=0.27 were obtained, which take into account the synergy and hybridity of modern threats, funding for the formation and improvement of the protection system, and also allow determining the financial and computing capabilities of the attacker based on the identified threats. The proposed method for assessing the security of cyber-physical systems is based on the developed threat classifier, allows assessing the current security level and provides recommendations regarding the allocation of limited protection resources based on an expert assessment of known threats. This approach allows offline dynamic simulation, which makes it possible to timely determine attackers' capabilities and form preventive protection measures based on threat analysis. In the simulation, actual bases for assessing real threats and incidents in cyber-physical systems can be used, which allows an expert assessment of their impact on both individual security services and security components (cyber security, information security and security of information). The presented simulation results do not contradict the graphical results of the classical Lotka–Volterra model, which indicates the adequacy of the proposed approach for assessing the security of cyber-physical systems

An Autonomous Cyber-Physical Anomaly Detection System Based on Unsupervised Disentangled Representation Learning

Cyber-Physical Systems (CPS) in heavy industry are a combination of closely integrated physical processes, networking, and scientific computing. The physical production process is monitored and controlled by the CPS in question, through advanced real-time networking systems, where high-precision feedback loops can be changed when the overgrid of cooperative computing and communication components that make up the industrial process is required. These CPS operate independently but integrate interaction capabilities as well as with the external environment, creating the connection of the physical with the digital world. The outline is that the most effective modeling and development of high-reliability CPS are directly related to the maximization of the production process, extroversion, and industrial competition. In this paper, considering the high importance of the operational status of CPS for heavy industry, an innovative autonomous anomaly detection system based on unsupervised disentangled representation learning is presented. It is a temporal disentangled variational autoencoder (TDVA) which, mimicking the process of rapid human intuition, using high- or low-dimensional reasoning, finds and models the useful information independently, regardless of the given problem. Specifically, taking samples from the real data distribution representation space, separating them appropriately, and encoding them as separate disentangling dimensions create new examples that the system has not yet dealt with. In this way, first, it utilizes information from potentially inconsistent sources to learn the right representations that can then be broken down into subspace subcategories for easier and simpler categorization, and second, utilizing the latent representation of the model, it performs high-precision estimates of how similar or dissimilar the inputs are to each other, thus recognizing, with great precision and in a fully automated way, the system anomalies.

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

With the rapid development of computing, communication, and control technologies, cyber–physical systems that integrate physical space, information space, and social space have emerged and are widely used in various important infrastructures. This article introduces the composition and basic algorithm of the hidden Markov model, and gives the mathematical description of the hidden Markov model. Since the hidden Markov model can deduce the hidden state of the observation object through the observed feature values, a device operating state cognition scheme based on the hidden Markov model is proposed. A method for analyzing cascading failures is proposed, and the critical threshold value of cyber–physical system under random attack is obtained. It is verified by simulation experiments, and the changes of system critical thresholds under different network parameters are compared and analyzed. We mainly use several sets of simulation experiments to verify the reliability of the critical threshold, and then verify near the critical threshold. Before simulating the cascading failure process, we first construct two random networks based on the average degree and the number of nodes. According to the previous description of the cyber–physical system model, a node in network B is randomly connected with three nodes in network A, so that the two networks are connected together to form a coupled system. Random attack or failure is represented by randomly deleting nodes. In the simulation experiment, we will simulate the process of cascading failure at each step, and after each step of cascading failure, we output and save the number of remaining nodes. When no nodes in the two networks are deleted, the cascading failure will stop, and then we will verify the critical threshold through the data obtained from the analysis. This provides the support of related theories and methods for the design of stable and reliable cyber–physical systems.

Quantitative risk assessment of a hydrogen refueling station by using a dynamic physical model based on multi-physics system-level modeling

Numerous accidents in HRSs have been reported worldwide in accident databases; therefore, many researchers have performed quantitative risk assessments (QRAs) of HRSs to enable risk-informed decision making in determining the safety distances or risk mitigation measures. The HRSs, located in urban areas such as Tokyo in Japan, are situated in congested areas with tall buildings and high population density; thus, they have relatively narrow station areas. However, the QRAs are generally suitable for large plants such as nuclear power plants or chemical plants; therefore, relatively small plants or installations, such as HRSs, have not yet been considered as QRA objects. Hence, it is necessary to conduct detailed QRAs with risk analyses and reduce the applied uncertainties for relatively small plants or installations. We applied a model-based approach of risk assessment to model the HRS process using multi-physics system-level modeling and simulated a target system using Modelica—an equation-based, object-oriented modeling language that allows acausal modeling of complex cyber-physical systems The primary aim of this study was to conduct a QRA of an HRS based on multi-physics system-level modeling. First, we modeled the HRS components and physical relationships between the components using basic physical equations. Then, we elucidate a QRA based on the constructed model. The difference in the leakage rates due to the leak positions and dynamic behavior of the model parameters were calculated using the constructed model. Finally, we estimated the individual risks of all the scenarios and compared the resulting risk contours based on the constructed model that includes the hydrogen-fuel dynamic behavior with those based on the traditional model. These results indicate that it is possible to assess whether the risks around the station boundary are acceptable based on the scenario information obtained by evaluating the risks near the station.

A Semantic Model for Interacting Cyber-Physical Systems

We propose a component-based semantic model for Cyber-Physical Systems (CPSs) wherein the notion of a component abstracts the internal details of both cyber and physical processes, to expose a uniform semantic model of their externally observable behaviors expressed as sets of sequences of observations. We introduce algebraic operations on such sequences to model different kinds of component composition. These composition operators yield the externally observable behavior of their resulting composite components through specifications of interactions of the behaviors of their constituent components, as they, e.g., synchronize with or mutually exclude each other's alternative behaviors. Our framework is expressive enough to allow articulation of properties that coordinate desired interactions among composed components within the framework, also as component behavior. We demonstrate the usefulness of our formalism through examples of coordination properties in a CPS consisting of two robots interacting through shared physical resources.

Modeling and Vulnerability Analysis of Spatially Embedded Heterogeneous Cyber-Physical Systems With Functional Dependency

Modern infrastructures are always spatially embedded and coupled with auxiliary information systems to form heterogeneous cyber-physical systems (CPSs). In this paper, considering the spatial information and overlap of components, a heterogeneous cyber-physical system model with weak dependency is proposed, in which the two networks overlap geographically and have heterogeneous models. Based on the connectivity between their dependent cyber nodes and the control center, the physical edges are classified as controllable or uncontrollable, corresponding to global and local load transfer strategies, respectively. Combined with coordinated DoS (deny of service) attacks on cyber nodes, the system vulnerability under physical edge-removal and edge-overload scenarios is investigated. It can be concluded that although the two networks are weakly dependent without direct failure-induced dependency, a fragmented cyber system can still aggravate the failure of the physical system. In the edge-removal scenario, deliberately attacking high-load edges produces better results, while imposing extra load on low-load edges performs better in the edge-overload scenario. In addition, blocking specific cyber nodes that make attacked edges or their adjacent edges uncontrollable can effectively improve the attack effect. This model might yield insight into modeling and protecting spatial cyber-physical systems.

Features of virtual duplicate creation of the turning process

The interdisciplinary approach of MC-SUITE implementation of the Industry 4.0 concept and the main requirements for digital models (virtual duplicates) of cyber-physical systems regarding virtual processes compliance to the real ones and the possibility of real-time operation have been described. The main condition for creating an adequate dynamic model of technological processing system (TPS) interaction with the cutting process has been defined and a model for such interaction for the longitudinal turning has been developed. On example of developed turning process structural scheme the possibility of describing the interaction of the TPS input parameters (cutting depth and longitudinal feed) with the output parameters (deformations in the respective directions) due to changes in the model internal parameters in a form of cutting force components have been demonstrated. Such representation of the model allows virtual turning process adjusting by specifying the cutting stiffness and functional dependences for the cutting forces components based on the results of intelligent analytical processing of the monitoring results of the TPS operation.