Modeling Of Cyber-physical Systems Research Articles

СТРАТЕГІЯ РОЗВИТКУ ДИЗАЙН-ОСВІТИ В ПОВОЄННІЙ УКРАЇНІ: ТРАНСДИСЦИПЛІНАРНИЙ ПІДХІД

The activities of designers in the new conditions require a qualitatively new way of thinking. The features of training designers with a new level of professional thinking are considered. A set of problems related to the specifics of the process of design thinking formation has been studied as a means of adapting to social transformations in post-war Ukraine. And also as a means of creating and transmitting new ideas in a single cognitive space of dynamic events. Design, forming material and visual culture, becomes a tool for understanding the properties of the surrounding world and human capabilities. By creating a material, spatial, subject environment, design is a key tool for innovation and competition, growth and development. This is important for the effective revival of post-war Ukraine and its flourishing. Therefore, a triad of actions is strategically important for design education - the formation of hybrid design thinking in the learning process, the expansion of the worldview and mastering the principles of risk prediction. New tools and technologies are valuable for design education, which contribute to creative and innovative activity through the triadic technology of forming natural design thinking, forming balanced creative-critical thinking, as well as 3D visualization of information flows of various nature in a single cognitive space. The growth of the influence of algorithms and artificial intelligence in modern society has created a new reality that negatively affects mental health and thinking, human cognition and interpersonal communication. Therefore, the transformation of design education on a transdisciplinary basis will allow solving urgent problems in post-war Ukraine. In our opinion, the key to the formation of design thinking is the triadic technology for solving systemic problems, the use of which contributes to the formation of balanced creative-critical thinking. It is important to systematically analyze the viability of biological objects and objects in extreme conditions using a code-symbol and a code-algorithm. Note that the heuristic metamodels of complex dynamic systems (cyber-physical systems, fractal systems), as a rule, have the form of the Star of David. Harmonization of interrelations and inversion of harmoniously connected opposites in information flows of various nature makes it possible to form hybrid complex-systemic thinking in the learning process, using metaheuristic models of cyber-physical systems, cognitive computing and artificial intelligence look like the Star of David. Its new interpretation as a code-algorithm for finding a balance between creative and critical thinking contributes to the formation of innovative thinking in the process of design activity.

Performance modeling and quantitative evaluation for cyber-physical systems based on LTS

Performance modeling and quantitative evaluation for cyber-physical systems based on LTS

Modeling and Verification of Uncertain Cyber-Physical System Based on Decision Processes

Currently, there is uncertainty in the modeling techniques of cyber-physical systems (CPS) when faced with the multiple possibilities and distributions of complex system behavior. This uncertainty leads to the system’s inability to handle uncertain data correctly, resulting in lower reliability of the system model. Additionally, existing technologies struggle to verify the activity and safety of CPS after modeling, lacking a dynamic verification and analysis approach for uncertain CPS properties.This paper introduces a generalized possibility decision process as a system model. Firstly, the syntax and semantics of generalized possibility temporal logic with decision processes are defined. Uncertain CPS is extended by modeling it based on time-based differential equations and uncertainty hybrid time automaton. After that, model checking is performed on the properties of activity and safety using fuzzy linear time properties. Finally, a cold–hot hybrid constant-temperature system model is used for simulation experiments. By combining theory and experiments, this paper provides a new approach to the verification of uncertain CPS, effectively addressing the state explosion problem. It plays a crucial role in the design of uncertain CPS and offers a key solution for model checking in the presence of uncertainty.

A Modeling and Verification Method of Cyber-Physical Systems Based on AADL and Process Algebra

Cyber-Physical Systems (CPS) are the next generation of intelligent systems that integrate information control devices with physical resources. With increasingly close connections between CPS components and frequent interactions, potential defects grow exponentially, rendering the operating environment of CPS unreliable. Therefore, research on methods and theories to ensure the correctness, safety and reliability of CPS is not only an important research topic but also an inevitable challenge. In this paper, we propose a CPS modeling and verification method based on Architecture Analysis & Design Language (AADL) and process algebra to address this challenge. Due to the continuous, time-constrained, stochastic, uncertain and concurrent characteristics of CPS, this paper considers both flexibility and rigor in the modeling process. We first extend the ability of AADL to describe the multiple characteristics of CPS and propose Hybrid Probability-AADL (HP-AADL). Second, this paper introduces conditional execution, conditional interruption and probability operators into Temporal Calculus of Communication Systems (TCCS) and designs a new formal modeling language Hybrid Probability-Temporal Calculus of Communication Systems (HP-TCCS). However, HP-AADL is a semi-formal modeling language that cannot be directly used for formal verification, it cannot strictly guarantee the quality of the established CPS models, including its functional correctness and safety. Therefore, this paper proposes transformation rules from HP-AADL to HP-TCCS, which enables model checking of CPS models described in HP-AADL within HP-TCCS. Additionally, this paper designs a new formal specification language HPAT-Spatial Temporal Logic (HPAT-STL) based on Probabilistic Computation Tree Logic (PCTL) and Spatial Logic, which characterizes the temporal, probabilistic and spatial properties of CPS model. To achieve formal verification of HP-TCCS model and HPAT-STL formulas, this paper proposes a model checking algorithm HPAT-Model Checking Algorithm (HPAT-MCA). Finally, we discuss a typical CPS example to demonstrate the effectiveness of our proposed method in ensuring correct, safe and reliable CPS.

Robust inter-reliant resilience of cyber-physical smart grids

Nowadays, information and cyber technologies have been integrated into traditional energy systems to enhance their energy performance. While this integration can improve the cost-benefit of smart grids, it may not fully capture the benefits in extreme conditions or during cascading outages.Electrical and cyber-physical energy systems have become more interconnected due to integration, which has also resulted in electrical energy systems (EESs) being more vulnerable to extreme natural disasters. The other system can be infiltrated by an outage in one system, leading to a cascading outage process. To tackle these challenges, a robust model of cyber-physical energy systems constrained by resilience considerations has been proposed to model the interdependences of EESs and cyber-physical systems (CPS) (i.e., EES & CPSs) under extreme natural disasters. The objective function of the proposed model is to improve cost-benefit and energy performance under extreme natural disasters. In this paper, it is explained how the energy efficiency for electrical energy systems can be improved under extreme natural disasters by the proposed operation processes for bulk integrated EESs & CPS. However, the highly nonlinear integrated EESs & CPS model is linearized to moderate the model complexity. Finally, the effectiveness of the proposed integrated EESs & CPS model is evaluated by investigating the modified IEEE-30 bus energy system.

Research on cyber-physical system models of downhole locomotive dispatching and fault

Fault diagnosis is of great significance for the rapid recovery of the system after the accident. Intelligent dispatching among multiple controllers is developed in this work to improve the efficiency of downhole locomotive dispatching, finally realizing unmanned intelligent locomotive dispatching. The locomotive dispatching logic processing model of with signal lights, rutting machine, locomotives, and other equipment is established based on the model theory of Cyber-physical systems (CPS). The model tasks in this system are combined with the embedded controller tasks which make it convenient to optimize the logic processing procedures using Ptolemy II software. Our key findings indicate that the proposed model can make the locomotive dispatching process without deadlock and make the processing procedure more reasonable. Simulation and practical experiments demonstrate that the controller cooperation speed can be improved after the CPS model is built, which can realize safer and more effective automatic locomotive dispatching.

An Efficient Hybrid Deep Learning Model for Denial of Service Detection in Cyber Physical Systems

Security is critical in the Cyber-Physical Systems (CPS) model for smart healthcare networks, and it will likely have a significant impact on the industry, medical and healthcare; and farming-related substructures shortly. Due to an increase in the frequency of security and privacy attacks in present times in healthcare networks, this article addressed a fundamental component of intrusion detection systems (IDS) based on the important parameter security. The limitations of IDS in reacting to cyberattacks as well as in establishing private controls in the field of smart healthcare have motivated this research. An efficient and lightweight deep learning-based CNN-Bidirectional LSTM is proposed for the DDoS detection that uses the features of Convolutional Neural Networks (CNNs) to classify traffic flows as benign and malicious in this study. The results are achieved using Python where four convolutional layers, Maximum Pooling, that ends with the Dense Layer. The hyperparameters used are batch size of 500, epochs 20, number of classes 25, and Relu and softmax pooling activation function along with the softmax

A Hybrid Threat Model for Smart Systems

Cyber-physical systems and their smart components have a pervasive presence in all our daily activities. Unfortunately, identifying the potential threats and issues in these systems and selecting enough protection is challenging given that such environments combine human, physical and cyber aspects to the system design and implementation. Current threat models and analysis do not take into consideration all three aspects of the analyzed system, how they can introduce new vulnerabilities or protection measures to each other. In this work, we introduce a novel threat model for cyber-physical systems that combines the cyber, physical, and human aspects. Our model represents the system's components relations and security properties by taking into consideration these three aspects. Together with the threat model we also propose a threat analysis method that allows understanding the security state of the system's components. The threat model and the threat analysis have been implemented into an automatic tool, called TAMELESS, that automatically analyzes threats to the system, verifies its security properties, and generates a graphical representation, useful for security architects to identify the proper prevention/mitigation solutions. We show and prove the use of our threat model and analysis with three cases studies from different sectors.

Joint Watermarking-Based Replay Attack Detection for Industrial Process Operation Optimization Cyber-Physical Systems

This paper addresses the replay attack detection issue for a class of industrial process operation optimization (IPOO) cyber-physical systems (CPSs). In contrast to conventional CPSs with a single loop structure, the IPOO CPSs employ a dual-layer network environment consisting of a wireless network for the setpoint optimization loop and a CAN bus for the device control loop. Consequently, the issue of attack detection is more complicated for the IPOO CPSs, and this motivates our current research. First, a unified model of the IPOO CPS is built, with PI controller controlling the physical plants and LQG controller managing setpoint optimization. Then, a novel joint watermarking detection mechanism is established with the PI controller watermarking, LQG controller watermarking, and a watermarking compensator. The proposed watermarking compensator with an augmented Kalman filter is utilized to efficiently eliminate false alarms brought on by information interactions of the coupled cyber-layers, enabling the accurate detection and location of replay attacks. Furthermore, a linear relationship is established between watermarking parameters and system performance loss. Finally, simulations with a quadruple water tank system are conducted to verify the effectiveness of the proposed algorithm.

Modeling and Analysis of Cyber-physical Systems Based on Petri Net

Modeling and Analysis of Cyber-physical Systems Based on Petri Net

Unified modelling for continuous–discrete hybrid adaptive machining CPS of large thin-walled parts

ABSTRACT Traditional machining is transforming to digital and intelligent machining, in which adaptive machining cyber-physical system (CPS) provides a useful approach to control the machining quality of large thin-walled parts. And the running of adaptive machining CPS is a complex multi-processes execution flow, which can be regarded as a continuous–discrete hybrid system. To realise adaptive controlling of machining quality and adaptive managing of process flow, a unified model for continuous–discrete hybrid adaptive machining CPS is constructed. Firstly, an architecture of adaptive machining CPS is proposed. Next, the cutting process in adaptive machining CPS is modelled as a continuous-variable system (CVS), while the process flow in adaptive machining CPS is modelled as a discrete-events system (DES). Then, the finite state machine is adopted to integrate the CVS and DES to form the unified model of adaptive machining CPS. Finally, an adaptive machining CPS is developed based on the unified model, and the machining results demonstrate that machining quality is efficiently controlled, as well as the process flow is orderly managed. The built unified model has four features, respectively universality, integrability, scalability, and reconfigurability, which can be reconstructed to form a new instancing model according to the different machining requirements.

An intelligent context-aware threat detection and response model for smart cyber-physical systems

Smart cities, businesses, workplaces, and even residences have all been converged by the Internet of Things (IoT). The types and characteristics of these devices vary depending on the industry 4.0 and have rapidly increased recently, especially in smart homes. These gadgets can expose users to serious cyber dangers because of a variety of computing constraints and vulnerabilities in the security-by-design concept. The smart home network testbed setup presented in this study is used to evaluate and validate the protection of the smart cyber-physical system. The context-aware threat intelligence and response model identifies the states of the aligned smart devices to distinguish between real-world typical and attack scenarios. It then dynamically writes specific rules for protection against potential cyber threats. The context-aware model is trained on IoT Research and Innovation Lab - Smart Home System (IRIL-SHS) testbed dataset. The labeled dataset is utilized to create a random forest model, which is subsequently used to train and test the context-aware threat intelligence SHS model's effectiveness and performance. Finally, the model's logic is used to gain rules to be included in Suricata signatures and the firewall rulesets for the response system. Significant values of the measuring parameters were found in the results. The presented model can be used for the real-time security of smart home cyber-physical systems and develops a vision of security challenges for Industry 4.0.

COMPARISON OF RESULTS OF NUMERICAL ANALYSIS OF SIMULATION OF CYBERPHYSICAL BIOSENSOR SYSTEMS

The article compares the results of numerical modeling of mathematical models of cyber-physical biosensor systems using lattice difference equations with time delay on hexagonal and rectangular lattices. The main attention is paid to the mathematical description of the dynamics of a discrete population in combination with the dynamic logic of the studied models. Lattice difference equations with delay are proposed for modeling the antigen-antibody interaction in hexagonal and rectangular biopixels. Similar to the diffusion phenomenon, spatial operators are considered to model the interaction between biopixels. The paper compares the results of numerical modeling of processes occurring in biopixels of cyberphysical biosensor systems in the form of phase plane images and lattice images of the probability of antigen-antibody bonds. Based on the obtained experimental results, a comparative analysis of the stability of mathematical models was carried out using lattice difference equations on hexagonal and rectangular lattices for cyberphysical biosensors.

Cyber-physical oil spill monitoring and detection for offshore petroleum risk management service

Petroleum industry has started to embrace the advanced petroleum cyber-physical system (CPS) technologies. Offshore petroleum CPS is particularly hard to build, mainly due to the difficulty in detecting and preventing offshore oil leaking. During the oil exploration and transportation process, the remote multi-sensing technology is typically employed for emerging service. It can be utilized for leak detection by enabling the underwater modeling of an offshore petroleum CPS. However, such a technology suffers from insufficient remote sensing resources and expensive computational overhead. In this work, a cross-entropy based leak detection technique is proposed to detect the oil leak, which facilitates the understanding of the oil leak induced marine pollution. Furthermore, a hierarchical parallel approach is proposed on the super computer Tianhe-2 to improve the efficiency of the proposed leak detection technique. Experimental results on Penglai oil spill events demonstrate that the proposed method can effectively identify the sources of oil spilling with accuracy up to 100%.

Analysis of Failure Propagation in Cyber-Physical Power Systems Based on an Epidemic Model

From the perspective of propagation dynamics in complex networks, failure propagation in cyber-physical power systems is analogous to the spread of diseases; subsequently, the cyber nodes and power nodes are regarded as individuals in each of their groups. In this study, a two-layer interdependent network model of the cyber-physical power system is proposed, where each subnetwork adopts the Susceptible-Infected-Susceptible (SIS) epidemic-spreading model. On this basis, we construct a failure cooperation propagation model of cyber-physical power systems. Furthermore, we introduce the node protection mechanism to ensure the normal operation of key nodes. The generated scale-free cyber network and IEEE118-bus power system are used for simulation to analyze the influence of the coupling effect between them on the final failure scale.

A Quantitative Risk Assessment Model for Distribution Cyber-Physical System Under Cyberattack

An accurate and comprehensive risk assessment in a distribution cyber-physical system (DCPS) is essential to ensure its smooth operation, effective control, and exposure of hidden dangers and security implications. Unfortunately, prior risk assessment methods are mostly limited in two aspects. First, the current studies do not respect the complex network topology and intertwined devicewise dependencies, thereby failing to delineate and model the risk propagation mechanism in DCPS accurately. Second, the prior work tends to focus on gauging the risks underlying physical systems independently, overlooking the quantification of risk impact on physical systems incurred by the cyberattack. To fill the gap, in this article, we propose a unified risk assessment model that gauges the comprehensive security implication of DCPS in a Bayesian regime, in which the three key components, namely, the prior probability, posterior probability, and minimum load-loss ratio, are calculated via the cumulative densities, epidemic model, and optimal load shedding, respectively. We benchmark our proposed model on a public testbed, IEEE 39-bus system, with extensive experiments. The results substantiate the viability and effectiveness of our model and suggest that the vulnerability of DCPS is positively correlated with the three components in our Bayesian modeling. We hope this finding can shed some light on future research by providing a plausible apparatus for measuring the security implications of physical systems in DCPS under cyberattacks.

Separation of learning and control for cyber–physical systems

Most cyber–physical systems (CPS) encounter a large volume of data which is added to the system gradually in real time and not altogether in advance. In this paper, we provide a theoretical framework that yields optimal control strategies for such CPS at the intersection of control theory and learning. In the proposed framework, we use the actual CPS, i.e., the “true” system that we seek to optimally control online, in parallel with a model of the CPS that is available. We then institute an information state for the system which does not depend on the control strategy. An important consequence of this independence is that for any given choice of a control strategy and a realization of the system’s variables until time t, the information states at future times do not depend on the choice of the control strategy at time t but only on the realization of the decision at time t, and thus they are related to the concept of separation between estimation of the state and control. Namely, the future information states are separated from the choice of the current control strategy. Such control strategies are called separated control strategies. Hence, we can derive offline the optimal control strategy of the system with respect to the information state, which might not be precisely known due to model uncertainties or complexity of the system, and then use standard learning approaches to learn the information state online while data are added gradually to the system in real time. We show that after the information state becomes known, the separated control strategy of the CPS model derived offline is optimal for the actual system. We illustrate the proposed framework in a dynamic system consisting of two subsystems with a delayed sharing information structure.

Integrating the Manufacturer Usage Description Standard in The Modelling of Cyber-Physical Systems

Integrating the Manufacturer Usage Description Standard in The Modelling of Cyber-Physical Systems

Coupled Model and Node Importance Evaluation of Electric Power Cyber-Physical Systems Considering Carbon Power Flow

To improve the distributed carbon emission optimization control capability of the smart distribution network system, thereby reducing the carbon emissions in the distribution process, it is a very important issue to comprehensively analyze the importance of the node carbon emission flow of the smart distribution network. This paper transforms the power grid into a carbon emission flow network through power flow calculations: Based on the complex network theory, it determines the coupling scale of the two networks by means of the correlation coefficient method and the correlation matrix method, and establishes a coupling network model based on the carbon emission flow network; Combining the different business characteristics of carbon emission flow and information flow, an evaluation index system considering the dual-network coupling scale is established, and a multi-indicator comprehensive evaluation method that combines the Topsis and grey relational analysis method, that can objectively evaluate indicators that contain subjective components was proposed; The obtained node importance values can be used to determine the relative key line, greater sum node importance values represent a greater carbon emission impact of the line, providing a sequential basis for the carbon reduction and restructuring of the distribution network; Taking the 3-machine 9-node system as an example, the carbon flow distribution in the corresponding network is calculated, and the comprehensive importance value of the coupling node is calculated to analyze the rationality of this method.

Developing Deep Learning Models for System Remaining Useful Life Predictions: Application to Aircraft Engines

Prognostics and health management (PHM) is an important part of ensuring reliable operations of complex safety- critical systems. System-level remaining useful life (RUL) estimation is a much more complex problem than making estimations at the component level, and system-level RUL methodologies remain sparse in the literature. Model-based approaches have traditionally worked in the past for components such as capacitors, MOSFETs, batteries, or hard-drives (to name a few examples), but developing high fidelity dynamics models of cyber physical systems that can be used to study the effects of multiple degrading components in the system remains a challenging task. Some initial work on model-based System RUL predictions was demonstrated in Khorasgani, et al [1], but, to generalize the system-level prognostics problem, we have to resort to pure data driven and hybrid approaches. In this work, we propose an end-to-end data- driven framework for developing deep learning models to predict remaining useful life of cyber physical systems operating under unknown faulty conditions. The raw data is organized with a data schema that improves the model development process anddown stream data analysis tasks. Due to the unknown faulty conditions, the raw sensor data is transformed into signals that expose the underlying degradation processes, which are then used for model development. Bayesian Optimization is used to tune the model parameters prior to training and validation. We show that this approach results in accurate predictions within 3 cycles to end of life (EOL). We demonstrate the effectiveness of our approach by applying it to the N-CMAPSS turbofan engine dataset recently released by NASA, which includes high fidelity degradation modeling, real world operating conditions, and a large set of fault operating modes.