Reliability Of Cyber-physical Systems Research Articles

RELIABLE OPERATION OF CYBER-PHYSICAL SYSTEM WITH ACCOMPANIED BY A DIGITAL TWIN

The reliability of cyber-physical systems (CPS) operation is based on the general problem minimization of multi-factor risks, the margin of permissible risk, the forecast of the destabilizing dynamics of risk factors, principles, hypotheses, and axioms that are directly related to the analysis of abnormal situations, accidents, and disasters. To ensure the reliable operation of the CPS, a digital twin is created, which accompanies the operation of the CPS throughout its life cycle. The design of digital twins is based on simulation modelling methods that provide the most realistic representation of a physical environment or object in the virtual world.

Real-time Cyber-Physical Security Solution Leveraging an Integrated Learning-Based Approach

Cyber-Physical Systems (CPS) has emerged as a paradigm that connects cyber and physical worlds, which provides unprecedented opportunities to realize intelligent applications such as smart home, smart cities, and smart manufacturing. However, CPS faces a great number of information security challenges (e.g., attacks) due to the integration of CPS as well as the human behaviors and interactions. Therefore, accurate and real-time attack detection and identification are essential to ensure information security and reliability of CPS. In this paper, we propose a novel integrated learning method that accurately detects an attack of a CPS system and then identifies the attack type in real time. Specifically, we consider a One-Class Support Vector Machine (OCSVM) model that only relies on the data from the normal state for training to achieve a real-time and effective detection of a CPS system state (i.e., normal or under-attack). If the system is detected to be under-attack, we then develop a Pairwise Self-supervised Long Short-Term Memory (PSLSTM) approach to identify the attack type, which aims to accurately distinguish the known attack types and discover unknown new attacks. Lastly, experimental results show the proposed method achieves promising performances compared with conventional and state-of-the-art learning-based benchmarks.

Enhancing Security and Energy Efficiency of Cyber-Physical Systems using Deep Reinforcement Learning

In the era of Industry 4.0, achieving both energy efficiency and robust security in Cyber-Physical Systems (CPSs) presents a significant challenge because of the resource requirements and complexity of these systems. This paper presents a novel method to integrate energy efficiency and robust security measures in CPSs. We propose the integration of anomaly detection techniques into the CPSs, to facilitate self-adaptation to changing conditions and threats, thereby enhancing system flexibility and reliability while also optimizing energy consumption. Our approach enhances the flexibility and reliability of CPSs by integrating Deep Reinforcement Learning (DRL) into the MAPE-K (Monitor-Analyze-Plan-Execute with Knowledge) control loop. This integration not only streamlines anomaly detection but also optimizes energy consumption, ensuring efficient and effective management of critical system functions. The outcome is a marked improvement in the adaptive decision-making capabilities of CPSs, leading to heightened security and better energy efficiency across various sectors and applications. This study significantly advances sustainable industrial practices within the Industry 4.0 paradigm, emphasizing the development of CPSs that excel in both energy efficiency and robust security.

Enhancing Security and Energy Efficiency of Cyber-Physical Systems using Deep Reinforcement Learning

In the era of Industry 4.0, achieving both energy efficiency and robust security in Cyber-Physical Systems (CPSs) presents a significant challenge because of the resource requirements and complexity of these systems. This paper presents a novel method to integrate energy efficiency and robust security measures in CPSs. We propose the integration of anomaly detection techniques into the CPSs, to facilitate self-adaptation to changing conditions and threats, thereby enhancing system flexibility and reliability while also optimizing energy consumption. Our approach enhances the flexibility and reliability of CPSs by integrating Deep Reinforcement Learning (DRL) into the MAPE-K (Monitor-Analyze-Plan-Execute with Knowledge) control loop. This integration not only streamlines anomaly detection but also optimizes energy consumption, ensuring efficient and effective management of critical system functions. The outcome is a marked improvement in the adaptive decision-making capabilities of CPSs, leading to heightened security and better energy efficiency across various sectors and applications. This study significantly advances sustainable industrial practices within the Industry 4.0 paradigm, emphasizing the development of CPSs that excel in both energy efficiency and robust security.

Reliability Optimization of CPS by digital twining

Digital twining (DT) is a recent approach in designing, optimization, and exploitation of cyber physical systems (CPS). DT can be used to simulate the behaviour of the CPS during a failure event, allowing reliability engineers to identify areas of the system that are vulnerable to failure, the root cause of the failure and to develop strategies for prevention. In this paper the interest goes to designing CPS based on digital twining approach while maximizing CPS reliability. The most common CPS reliability requirements are investigated and their reliability optimization strategies when using DT are discussed.

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.

AI-augmented Model-Based Capabilities in the AIDOaRt Project

The paper presents the AIDOaRT project, a 3 years long H2020-ECSEL European project involving 32 organizations, grouped in clusters from 7 different countries, focusing on AI-augmented automation supporting modeling, coding, testing, monitoring, and continuous development in Cyber-Physical Systems (CPS). To this end, the project proposes to combine Model Driven Engineering principles and techniques with AI-enhanced methods and tools for engineering more trustable and reliable CPSs. This paper introduces the AIDOaRt project, its overall objectives, and used requirement engineering methodology. Based on that, it also focuses on describing the current plan regarding a set of tools intended to cover the modelbased capabilities requirements from the project.

Reliability Assessment of Cyber–Physical Distribution Systems Considering Cyber Disturbances

With the development of communication technology, traditional distribution networks have gradually developed into cyber–physical systems (CPSs), from which the cyber system provides more protection for the grid and brings new security threat–cyber disturbances. Current research cannot scientifically measure the impact of cyber disturbances on the system and lacks reliability indices for a comprehensive quantitative assessment of CPS reliability from the perspective of cyber–physical fusion. If the impact of information disturbances on system reliability is not assessed accurately, it will not be possible to provide a scientific and reasonable decision basis for system planning and operation. Therefore, a set of reliability assessment methods and indices for distribution network CPSs considering cyber disturbances is proposed. Firstly, a reliability modeling method combining fault tree and Petri net is proposed to model the reliability of a distribution network CPS, which can improve the efficiency and accuracy of the modeling. Secondly, the system state is divided into two categories: normal operation state and cyberattack state. Then, generalized reliability indices considering cyber disturbances for distribution network CPSs are defined. Finally, through the tests on the modified IEEE RBTS BUS2 distribution network CPS, an analysis of the effects of information component failures, cyberattacks, and access network structures on system reliability is conducted in this paper to verify the efficiency of the proposed method and the rationality of the newly defined reliability indices.

Social Internet of Things: Ethical AI Principles in Trust Management

Trust management has become a fundamental requirement for Social Internet of Things (SIoT) to enable a trustworthy social network of smart objects necessary for enhancing the security and reliability of cyber-physical systems. To increase the credibility scores in trust management, AI (Artificial Intelligence) has been adopted. However, the current need for digital acceleration has brought ethical concerns related to the smartness and social consciousness of autonomous objects, which leads to a question whether AI-based trust management is ready to deal with these concerns. In this paper, we consider 11 ethical dimensions within the context of trust management in SIoT. Then, we examine the existing AI-based trust models in the context of SIoT and its related application domains to assess their maturity in terms of the 11 ethical dimensions. The evaluation results show how trust management can be improved by AI ethical principles in vehicular networks and underwater acoustic sensor networks.

What are the fundamental software abstractions for designing reliable cyber-physical systems operating in uncertain environments?

Cyber-physical systems (CPS) combine computations in embedded systems with physical dynamics, typically interconnected using networks. Predictability and reliability of combined software and hardware systems are crucial for CPS design (Lee, 2008), but all parts in a real CPS are not necessarily predictable. Specifically, operating in a physical environment introduces different aspects of uncertainty, including uncertainties in sensors, the physical dynamics of the system itself and interactions with other systems or humans. There is a large body of knowledge about reasoning with uncertainty in fields and topics such as statistics, automatic control and sensor fusion. However, how to program for uncertainty in complex cyber-physical systems needs to be better understood. What are the fundamental software abstractions? How do they compose and interact?

Diversity-by-Design for Dependable and Secure Cyber-Physical Systems: A Survey

Diversity-based security approaches have been studied for several decades since the 1970's. The concept of diversity-by-design emerged in the 1980's and, since then, diversity-based system design research has been explored to build more secure and dependable systems. In this work, we are particularly interested in providing an in-depth, comprehensive survey of existing diversity-based approaches, insights, and future work directions for those who want to conduct research on developing secure and dependable cyber-physical systems (CPSs) using diversity as a system design feature. To be specific, this survey paper provides: (i) The common concept of diversity based on a multidisciplinary study of diversity from nine different fields along with the historical evolution of diversity-by-design for security; (ii) The design principles of diversity-based approaches; (iii) The key benefits and caveats of using diversity-by-design; (iv) The key concerns of CPS environments in introducing diversity-by-design; (v) A variety of existing diversity-based approaches based on five different classifications; (vi) The types of attacks mitigated by existing diversity-based approaches; (vii) The overall trends of evaluation methodologies used in diversity-based approaches, in terms of metrics, datasets, and testbeds; and (viii) The insights, lessons, and gaps identified from this extensive survey.

A model of factors influencing deck officers’ cyber risk perception in offshore operations

Offshore operations onboard vessels are increasingly reliant on digitalization, integration, automation, and networked-based systems, which creates new dimensions of cyber risks. The causes of cyber incidents often include complex relationships between humans and technology, and in offshore operations, the onboard crew can be both a cyber security risk and a vital resource in strengthening the cyber security. This makes the behaviour of the decisionmakers onboard important in both preventing and handling cyber risks at sea. By use of in-depth interviews and the constant comparative analysis (CCA), this paper investigates factors influencing deck officers’ cyber risk perception in offshore operations and presents a contextual model of these factors. The model indicates that deck officers’ cyber risk perception can be affected by a feeling of distance towards cyber risks, being more restricted in their working environment because of digitalization, and trust in their reliable cyber-physical systems and suppliers. Further, targeted cyber risk mitigation measures should be implemented on multiple levels in shipping companies. The measures may benefit from focusing on increased risk communication, operational training, awareness campaigns, vessel-specific procedures, and policies, in addition to increased communication from management regarding the demand for digitalization. With this approach, the contextual model can contribute to the ongoing work of developing targeted measures for cyber risk mitigation in the maritime domain and can be used as a point of departure for further studies to discover additional nuances and factors within cyber risk perception in this domain.

Safe-visor architecture for sandboxing (AI-based) unverified controllers in stochastic cyber–physical systems

High performance but unverified controllers, e.g., artificial intelligence-based (a.k.a. AI-based) controllers, are widely employed in cyber–physical systems (CPSs) to accomplish complex control missions. However, guaranteeing the safety and reliability of CPSs with this kind of controllers is currently very challenging, which is of vital importance in many real-life safety-critical applications. To cope with this difficulty, we propose in this work a Safe-visor architecture for sandboxing unverified controllers in CPSs operating in noisy environments (a.k.a. stochastic CPSs). The proposed architecture contains a history-based supervisor, which checks inputs from the unverified controller and makes a compromise between functionality and safety of the system, and a safety advisor that provides fallback when the unverified controller endangers the safety of the system. Both the history-based supervisor and the safety advisor are designed based on an approximate probabilistic relation between the original system and its finite abstraction. By employing this architecture, we provide formal probabilistic guarantees on preserving the safety specifications expressed by accepting languages of deterministic finite automata (DFA). Meanwhile, the unverified controllers can still be employed in the control loop even though they are not reliable. We demonstrate the effectiveness of our proposed results by applying them to two (physical) case studies.

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.

Guaranteeing Timely Response to Changes of Monitored Objects by Assigning Deadlines and Periods to Tasks

Timely response to changes of monitored objects is the key to ensuring the safety and reliability of cyber-physical systems (CPSs). There are two kinds of tasks in CPSs: update tasks and control tasks. Update tasks are responsible for updating the data in the system based on the state of the objects they monitor. Control tasks are responsible for making decisions based on the data in the system. The response time of the system to the change of a monitored object consists of two parts: the time taken by update tasks to reflect the change to the system, and the time taken by control tasks to make decisions according to the data in the system. Deadlines and periods of update tasks and control tasks directly affect the response time. Reasonable deadline and period assignment is the key to ensuring timely response to the changes of monitored objects. In this paper, we study the deadline and period assignment in CPSs. To the best of our knowledge, all existing work only focuses on the deadline and period assignment for update tasks with the goal of ensuring the freshness of the data in CPSs, and this is the first study focusing on the deadline and period assignment for both update tasks and control tasks with the goal of ensuring timely response to the changes of monitored objects. A new problem about response time control and system workload control is defined in this paper. Two deadline and period assignment methods are proposed to solve the defined problem. All the proposed methods can be used in the CPSs adopting the earliest deadline first (EDF) scheduling method. Experiments with randomly generated tasks are conducted to evaluate the performance of the proposed methods in terms of acceptance ratio and execution efficiency.

Nonlinearity, Automation, and Reliable Cyberphysical Systems

The challenge of reliable cyberphysical system (CPS) engineering requires design automation methods that fully exploit algorithmic and datadriven approaches to NP-hard problem solving. Full-stack synthesis tools that integrate design, implementation, and certification are crucial for developing future generations of CPSs with rigorous functionality and reliability guarantees.

Security risks in cyber physical systems—A systematic mapping study

AbstractThe increased need for constant connectivity and complete automation of existing systems fuels the popularity of Cyber Physical Systems (CPS) worldwide. Increasingly more, these systems are subjected to cyber attacks. In recent years, many major cyber‐attack incidents on CPS have been recorded and, in turn, have been raising concerns in their users' minds. Unlike in traditional IT systems, the complex architecture of CPS consisting of embedded systems integrated with the Internet of Things (IoT) requires rather extensive planning, implementation, and monitoring of security requirements. One crucial step to planning, implementing, and monitoring of these requirements in CPS is the integration of the risk management process in the CPS development life cycle. Existing studies do not clearly portray the extent of damage that the unattended security issues in CPS can cause or have caused, in the incidents recorded. An overview of the possible risk management techniques that could be integrated into the development and maintenance of CPS contributing to improving its security level in its actual environment is missing. In this paper, we are set out to highlight the security requirements and issues specific to CPS that are discussed in scientific literature and to identify the state‐of‐the‐art risk management processes adopted to identify, monitor, and control those security issues in CPS. For that, we conducted a systematic mapping study on the data collected from 312 papers published between 2000 and 2020, focused on the security requirements, challenges, and the risk management processes of CPS. Our work aims to form an overview of the security requirements and risks in CPS today and of those published contributions that have been made until now, towards improving the reliability of CPS. The results of this mapping study reveal (i) integrity authentication and confidentiality as the most targeted security attributes in CPS, (ii) model‐based techniques as the most used risk identification and assessment and management techniques in CPS, (iii) cyber‐security as the most common security risk in CPS, (iv) the notion of “mitigation measures” based on the type of system and the underline internationally recognized standard being the most used risk mitigation technique in CPS, (v) smart grids being the most targeted systems by cyber‐attacks and thus being the most explored domain in CPS literature, and (vi) one of the major limitations, according to the selected literature, concerns the use of the fault trees for fault representation, where there is a possibility of runtime system faults not being accounted for. Finally, the mapping study draws implications for practitioners and researchers based on the findings.

A Review of Current Research Trends in Power-Electronic Innovations in Cyber–Physical Systems

In this article, a broad overview of the current research trends in power-electronic innovations in cyber–physical systems (CPSs) is presented. The recent advances in semiconductor device technologies, control architectures, and communication methodologies have enabled researchers to develop integrated smart CPSs that can cater to the emerging requirements of smart grids, renewable energy, electric vehicles, trains, ships, the Internet of Things (IoT), and so on. The topics presented in this article include novel power-distribution architectures, protection techniques considering large renewable integration in smart grids, wireless charging in electric vehicles, simultaneous power and information transmission, multihop network-based coordination, power technologies for renewable energy and smart transformer, CPS reliability, transactive smart railway grid, and real-time simulation of shipboard power systems. It is anticipated that the research trends presented in this article will provide a timely and useful overview to the power-electronics researchers with broad applications in CPSs.

A Human-centric and Environment-aware Testing Framework for Providing Safe and Reliable Cyber-Physical System Services

The functions, capabilities, and effects produced by the application services of cyber physical systems (CPS) are usually consumed by users performing their daily activities in a variety of environmental conditions. Thus, it is critical to ensure that those systems neither interfere with human activities nor harm the users involved. In this paper, we propose a framework for testing and verifying the safety and reliability of CPS services from the perspectives of CPS environments and users. The framework provides an environmentaware testing method by which the efficiency of testing CPS services can be improved by prioritizing CPS environments and by applying machinelearning techniques. The framework also includes a metric by which we can automate the test of the most effective services that deliver effects from physical devices to users. Additionally, the framework provides a computational model that assesses mental workloads to test whether a CPS service can cause cognitive depletion or contention problems for users. We conducted a series of experiments to show the effectiveness of the proposed approaches for ensuring the safety and reliability of CPS application services during the development and operation phases.

Trustworthiness in Industrial IoT Systems Based on Artificial Intelligence

The intelligent industrial environment developed with the support of the new generation network cyber-physical system (CPS) can realize the high concentration of information resources. In order to carry out the analysis and quantification for the reliability of CPS, an automatic online assessment method for the reliability of CPS is proposed in this article. It builds an evaluation framework based on the knowledge of machine learning, designs an online rank algorithm, and realizes the online analysis and assessment in real time. The preventive measures can be taken timely, and the system can operate normally and continuously. Its reliability has been greatly improved. Based on the credibility of the Internet and the Internet of Things, a typical CPS control model based on the spatiotemporal correlation detection model is analyzed to determine the comprehensive reliability model analysis strategy. Based on this, in this article, we propose a CPS trusted robust intelligent control strategy and a trusted intelligent prediction model. Through the simulation analysis, the influential factors of attack defense resources and the dynamic process of distributed cooperative control are obtained. CPS defenders in the distributed cooperative control mode can be guided and select the appropriate defense resource input according to the CPS attack and defense environment.