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The safety of neural network (NN) controllers is crucial, specifically in the context of safety-critical Cyber-Physical System (CPS) applications. Current safety verification focuses on the reachability analysis, considering the bounded errors from the noisy environments or inaccurate implementations. However, it assumes real-valued arithmetic and does not account for the fixed-point quantization often used in the embedded systems. Some recent efforts have focused on generating the sound quantized NN implementations in fixed-point, ensuring specific target error bounds, but they assume the safety of NNs is already proven. To bridge this gap, we introduce Nexus, a novel two-phase framework combining reachability analysis with sound NN quantization. Nexus provides an end-to-end solution that ensures CPS safety within bounded errors while generating mixed-precision fixed-point implementations for the NN controllers. Additionally, we optimize these implementations for the automated parallelization on the FPGAs using a commercial HLS compiler, reducing the machine cycles significantly. [ABSTRACT FROM AUTHOR]

Integration of the Internet of Things (IoT) in industrial settings necessitates robust cybersecurity measures to mitigate risks such as data leakage, vulnerability exploitation, and compromised information flows. Recent cyberattacks on critical industrial systems have highlighted the lack of threat analysis in software development processes. While existing threat modeling frameworks such as STRIDE enumerate potential security threats, they often lack detailed mapping of the sequences of threats that adversaries might exploit to apply cyberattacks. Our study proposes an enhanced approach to systematic threat modeling and data flow-based attack scenario analysis for integrating cybersecurity measures early in the development lifecycle. We enhance the STRIDE framework by extending it to include attack scenarios as sequences of threats exploited by adversaries. This extension allows us to illustrate various attack scenarios and demonstrate how these insights can aid system designers in strengthening their defenses. Our methodology prioritizes vulnerabilities based on their recurrence across various attack scenarios, offering actionable insights for enhancing system security. A case study in the automotive industry illustrates the practical application of our proposed methodology, demonstrating significant improvements in system security through proactive threat modeling and analysis of attack impacts. The results of our study provide actionable insights to improve system design and mitigate vulnerabilities. [ABSTRACT FROM AUTHOR]

With advances in Cyber-Physical Systems (CPS), privacy-preserving and security issues have attracted substantial attention. A crucial function provided by CPS is anomaly detection on large-scale, complicated, and dynamic data. Physical and network information about the systems for safeguarding original data and identifying cyberattacks is needed in order to develop a reliable privacy-preserving anomaly detection approach. Conventional anomaly detection techniques cannot be directly used to solve these problems because they must deal with the expanding amount of data and need domain-specific expertise. By filtering and choosing key aspects from the original data for improved safety, this research presents a privacy preservation approach for secure anomaly detection. For selecting features, the Mutation Mayfly Algorithm (MMA) has been developed. The proposed program combines key benefits of swarm intelligence and evolutionary algorithms. The usage of MMA in feature selection results from its better accuracy and straightforward structure. Then, a strategy for identifying anomalies based on a Kalman Filter (KF) model and a Gaussian Mixture Model (GMM) has been created to find cyberattacks in CPS. Furthermore, the efficacy of privacy-preserving anomaly detection is being improved through the utilization of a Gaussian Mixture Model (GMM) to convert the noteworthy features into representative characteristics. The present study provides a description of the KF approach, which involves the analysis of the dynamics pertaining to both normal and attack events. The system employs a dynamic thresholding technique to detect anomalous behavior by calculating the lower and upper boundaries of normal activity. The architecture is assessed using two open datasets, UNSW-NB15 for network data and Power System for data on cyber power. [ABSTRACT FROM AUTHOR]

Underwater drones are vital for scientific research, environmental monitoring, and maritime operations, allowing data collection in challenging environments. However, their deployment faces issues such as low bandwidth, high latency, signal attenuation, and intermittent connectivity due to mobility and water currents. Traditional centralized data processing approaches are inefficient under these conditions as they require transmitting large volumes of raw data to a central location. To address these challenges, this study proposes a Federated Learning (FL) framework specifically tailored for underwater networks. Unlike centralized approaches, FL enables underwater drones to collaboratively train a global intrusion detection model by processing data locally and sharing only model updates with the central server. This approach significantly improves data security by ensuring that sensitive information never leaves the local devices, reducing the risk of interception or compromise during transmission. Furthermore, FL’s decentralized architectures inherently aligns with the dynamic and distributed nature of underwater drone networks. The proposed framework improves cyber intrusion detection by leveraging localized insights from individual drones to detect threats, including zero-day attacks, without directly exposing sensitive data. By preserving privacy and enabling collaborative anomaly detection, FL addresses key cybersecurity challenges in the Internet of Underwater Things (IoUT).

Abstract Smart cities have developed advanced technology that improves people’s lives. A collaboration of smart cities with autonomous vehicles shows the development towards a more advanced future. Cyber-physical system (CPS) are used blend the cyber and physical world, combined with electronic and mechanical systems, Autonomous vehicles (AVs) provide an ideal model of CPS. The integration of 6G technology with Autonomous Vehicles (AVs) marks a significant advancement in Intelligent Transportation Systems (ITS), offering enhanced self-sufficiency, intelligence, and effectiveness. Autonomous vehicles rely on a complex network of sensors, cameras, and software to operate. A cyber-attack could interfere with these systems, leading to accidents, injuries, or fatalities. Autonomous vehicles are often connected to broader transportation networks and infrastructure. A successful cyber-attack could disrupt not only individual vehicles but also public transportation systems, causing widespread chaos and economic damage. Autonomous vehicles communicate with other vehicles (V2V) and infrastructure (V2I) for safe and efficient operation. If these communication channels are compromised, it could lead to collisions, traffic jams, or other dangerous situations. So we present a novel approach to mitigating these security risks by leveraging pre-trained Convolutional Neural Network (CNN) models for dynamic cyber-attack detection within the cyber-physical systems (CPS) framework of AVs. The proposed Intelligent Intrusion Detection System (IIDS) employs a combination of advanced learning techniques, including Data Fusion, One-Class Support Vector Machine, Random Forest, and k-Nearest Neighbor, to improve detection accuracy. The study demonstrates that the EfficientNet model achieves superior performance with an accuracy of up to 99.97%, highlighting its potential to significantly enhance the security of AV networks. This research contributes to the development of intelligent cyber-security models that align with 6G standards, ultimately supporting the safe and efficient integration of AVs into smart cities.

Cyber-physical systems (CPS), along with information technology devices such as portable devices and robots, materials including knowledge contents, and Internet resources, can be integrated into curricula to guide learners in observing and experiencing STEM (science, technology, engineering, and mathematics) knowledge and skills. This integration provides opportunities for learners to cultivate their abilities to design creative works. The objective of this study was to investigate the impact of a robot-assisted teaching and learning system in STEM education on learners' academic knowledge, skills, and attitudes. To achieve this objective, an experimental design was employed, with 30 college students assigned to learn and create a Drawdio electronic sound synthesizer using robot teachers and tablet computers. Pre- and post-tests were administered to measure students' academic achievement in STEM, and a survey was conducted to assess their attitudes toward STEM. The findings of the study indicate that the combination of robot teachers and tablet computers served as a supplementary learning resource within the learning environment. It accelerated knowledge acquisition, expanded hands-on experiences, and improved engagement and motivation among the learners. These results suggest that robot-assisted teaching and learning systems have the potential to enhance learning outcomes and attitudes among students. However, it is important to note that human teachers still outperformed robot teachers in certain aspects, particularly in higher levels of knowledge acquisition according to Bloom's taxonomy. Therefore, this study suggests the adoption of a collaborative model involving both robot and human teachers to effectively promote STEM education in the classroom. [ABSTRACT FROM AUTHOR]

In this article, we investigate the problem of event‐triggered distributed consensus control for a class of complex cyber‐physical systems (CPSs), where unknown backlash‐like hysteresis inputs and denial‐of‐service (DoS) attacks are considered in actuators and communication channels, respectively. In the first place, when communication networks are subjected to malicious DoS attacks, the connection weights of network communication topologies experience disruptions. To solve this issue, this work proposes a solution by constructing topological models for communication networks employing switching topologies and utilizing observers to estimate the combined measurement errors during the time interval of DoS attacks. Secondly, to address the repercussions of DoS attacks on control units and to optimize the utilization of communication resources, a distributed control protocol based on an event‐triggered scheme and employing locally deployed estimators is designed to significantly decrease the update frequency of control signals. Meanwhile, utilizing Lyapunov stability theory, the boundedness of all signals in the closed‐loop system is validated, thereby accomplishing the goal of consensus control. At last, the efficacy of the proposed control scheme is demonstrated through two simulation examples. [ABSTRACT FROM AUTHOR]

In the rapidly evolving landscape of Industry 4.0, the demand for skilled professionals well-versed in Internet of Things (IoT) technologies is escalating. However, a significant challenge faced in educational settings is the lack of comprehensive and effective methodologies for imparting practical knowledge and skills in IoT. This paper presents an approach for designing and implementing an Internet of Things (IoT) laboratory in which students may practice and comprehend many components of an IoT environment, such as analysis of sensor data, IoT platform development, and setup of messaging protocols. The teaching methodology adopts a Cyber-Physical System (CPS) framework, which integrates teachers, classrooms, and resources to create a comprehensive learning environment. Bloom's taxonomy is employed to assess the efficacy of the suggested technique in terms of cognitive skills and knowledge acquisition. The evaluation procedure demonstrated the advantages of a hybrid learning environment, which integrated both face-to-face and remote instruction. The case study is conducted in an engineering higher education course on first year students. The findings of this paper lay the groundwork for a remotely deliverable IoT training course, contributing to the advancement of IoT education and equipping students with the necessary skills for the evolving landscape of Industry 4.0. [ABSTRACT FROM AUTHOR]

With the rapid development of new-generation cyber–physical system (CPS) technologies, the smart operation and management of the broad bean harvesting system are enabled. This paper proposed a smart broad bean harvesting system (SBHS) and a self-adaptive control method based on CPS technologies. Firstly, the overall architecture of the SBHS is designed, which consists of four main components, namely optimal intelligent perception environment configuration, digital twin model construction, virtual simulation and real-time optimization, self-adaptive adjustment and control. Then, three key enabling technologies are discussed in detail, namely, intelligent perception environment configuration for the SBHS, digital twin model construction for the SBHS, colored Petri net (CPN)-based self-adaptive analysis and control of the harvesting system. Lastly, a proof-of-concept experiment based on a cooperative company is presented to illustrate the main work logic and advantage of the proposed SBHS. After the edge–cloud cooperative intelligent harvesting environment is configured, the CPN model for the workflow of the SBHS is created to analyze and optimize the harvesting processes. In addition, a management and control platform are developed to further illustrate the implementation of the proposed SBHS and the self-adaptive control method. [ABSTRACT FROM AUTHOR]

In this rapidly advancing era, technology has been progressing extensively and swiftly. As a result, the emergence of numerous Cyber-Physical Systems (CPS) has become imperative to meet the technological demands of modern life. However, these systems generate a substantial amount of data, which poses challenges in terms of management, storage, and susceptibility to external attacks. This paper primarily focuses on the performance and security aspects of CPS, particularly in countering external threats, through the integration of blockchain technology and machine learning. It provides a comprehensive review of recent research findings that demonstrate the use of blockchain to enhance CPS performance while ensuring robust security. Furthermore, the paper explores the synergistic application of blockchain and machine learning techniques to reinforce CPS security. Moreover, it investigates how the combination of blockchain with physically unclonable functions (PUF) can significantly enhance the efficacy of physical device authentication. [ABSTRACT FROM AUTHOR]

The impact of communication disturbances on microgrids (MGs) needs robust and scalable Information Communication Technology (ICT) infrastructure for efficient MG control. This work builds on advances in the Internet of Things (IoT) to provide a practical platform for testing the impact of various cyber-attacks on a distributed control scheme for a Multi-Agent System (MAS). This paper presents a Controller Hardware-in-the-Loop (CHIL) testbed to investigate the impact of various cyber-attacks and communication disruptions on MGs. A distributed consensus secondary control scheme for a MAS within an MG cyber-physical system (CPS) is proposed. The proposed cyber-physical testbed integrates a real-time islanded AC microgrid on RT-Lab, secondary controllers implemented on single-board computers, and an attacker agent on another single-board computer. Communication occurs via a UDP/IP network between OPAL-RT and controller agents, as well as between the agents. Through meticulous experimentation, the efficacy of the proposed control strategy using the developed platform is validated. Various attacks were modeled and launched including deception attacks on sensors, actuators, and their combinations, as well as disruption attacks. The ramifications of both deception and disruption cyber-attacks on system performance are analyzed. [ABSTRACT FROM AUTHOR]

The convergence of cyber and physical systems through cyber–physical systems (CPSs) has been integrated into cyber–physical production systems (CPPSs), leading to a paradigm shift toward intelligent manufacturing. Despite the transformative benefits that CPPS provides, its increased connectivity exposes manufacturers to cyber-attacks through exploitable vulnerabilities. This paper presents a novel approach to CPPS security protection by leveraging digital twin (DT) technology to develop a comprehensive security model. This model enhances asset visibility and supports prioritization in mitigating vulnerable components through DT-based virtual tuning, providing quantitative assessment results for effective mitigation. Our proposed DT security model also serves as an advanced simulation environment, facilitating the evaluation of CPPS vulnerabilities across diverse attack scenarios without disrupting physical operations. The practicality and effectiveness of our approach are illustrated through its application in a human–robot collaborative assembly system, demonstrating the potential of DT technology. [ABSTRACT FROM AUTHOR]

Industrial and technological evolution has led to the identification of different techniques and strategies that can best adapt to the needs of Manufacturing Industry 4.0. As industrial production has become more automated, the need for more efficient maintenance strategies has increased. Today, among the possible, several applications demonstrate how the Predictive Maintenance (PdM) strategy is the best performing. In fact, PdM makes it possible to predict an impending failure with high accuracy in order to intervene before failure occurs. This work focuses on the application of PdM technique in order to predict the type of chips produced by a lathe through a machine learning algorithm. Moreover, being our application a delay-sensitive one, to drastically decrease the time delay in prediction, our solution proposes the combination of PdM with the Edge Computing paradigm. To simulate this paradigm, the chosen machine learning models were deployed on STM microcontrollers obtaining both high accuracy (98%) and an inference time in the order of milliseconds.

Abstract Cyber-physical systems (CPSs) are emergent systems that enable effective real-time communication and collaboration (C&C) of physical components such as control systems, sensors, actuators, and the surrounding environment through a cyber communication infrastructure. As such, autonomous vehicles (AVs) are one of the fields that have significantly adopted the CPS approach to improving people's lives in smart cities by reducing energy consumption and air pollution. Therefore, autonomous vehicle-cyber physical systems (AV-CPSs) have attracted enormous investments from major corporations and are projected to be widely used. However, AV-CPS is vulnerable to cyber and physical threat vectors due to the deep integration of information technology (IT), including cloud computing, with the communication process. Cloud computing is critical in providing the scalable infrastructure required for real-time data processing, storage, and analysis in AV-CPS, allowing these systems to work seamlessly in smart cities. CPS components such as sensors and control systems through network infrastructure are particularly vulnerable to cyber-attacks targeted by attackers using the communication system. This paper proposes an intelligent intrusion detection system (IIDS) for AV-CPS using transfer learning to identify cyberattacks launched against connected physical components of AVs through a network infrastructure. First, AV-CPS was developed by implementing the controller area network (CAN) and integrating it into the AV simulation model. Second, the dataset was generated from the AV-CPS. The collected dataset was then preprocessed to be trained and tested via pre-trained CNNs. Third, eight pre-trained networks were implemented, namely, InceptionV3, ResNet-50, ShuffleNet, MobileNetV2, GoogLeNet, ResNet-18, SqueezeNet, and AlexNet. The performance of the implemented models was evaluated. According to the experimental evaluation results, GoogLeNet outperformed all other pre-rained networks, scoring an F1- score of 99.47%.

Cyber-Physical Systems (CPS) continuously interact with their physical environments through embedded software controllers that observe the environments and determine actions. Field Operational Tests (FOT) are essential to verify to what extent the CPS under analysis can achieve certain CPS goals, such as satisfying the safety and performance requirements, while interacting with the real operational environment. However, performing many FOTs to obtain statistically significant verification results is challenging due to its high cost and risk in practice. Simulation-based verification can be an alternative to address the challenge, but it still requires an accurate virtual environment model that can replace the real environment interacting with the CPS in a closed loop. In this article, we propose ENVI (ENVironment Imitation), a novel approach to automatically generate an accurate virtual environment model, enabling efficient and accurate simulation-based CPS goal verification in practice.To do this, we first formally define the problem of the virtual environment model generation and solve it by leveraging Imitation Learning (IL), which has been actively studied in machine learning to learn complex behaviors from expert demonstrations. The key idea behind the model generation is to leverage IL for training a model that imitates the interactions between the CPS controller and its real environment as recorded in (possibly very small) FOT logs. We then statistically verify the goal achievement of the CPS by simulating it with the generated model. We empirically evaluate ENVI by applying it to the verification of two popular autonomous driving assistant systems. The results show that ENVI can reduce the cost of CPS goal verification while maintaining its accuracy by generating accurate environment models from only a few FOT logs. The use of IL in virtual environment model generation opens new research directions, further discussed at the end of the article. [ABSTRACT FROM AUTHOR]

Cyber-physical systems (CPSs) are emergent systems that enable effective real-time communication and collaboration (C&C) of physical components such as control systems, sensors, actuators, and the surrounding environment through a cyber communication infrastructure. As such, autonomous vehicles (AVs) are one of the fields that have significantly adopted the CPS approach to improving people's lives in smart cities by reducing energy consumption and air pollution. Therefore, autonomous vehicle-cyber physical systems (AV-CPSs) have attracted enormous investments from major corporations and are projected to be widely used. However, AV-CPS is vulnerable to cyber and physical threat vectors due to the deep integration of information technology (IT), including cloud computing, with the communication process. Cloud computing is critical in providing the scalable infrastructure required for real-time data processing, storage, and analysis in AV-CPS, allowing these systems to work seamlessly in smart cities. CPS components such as sensors and control systems through network infrastructure are particularly vulnerable to cyber-attacks targeted by attackers using the communication system. This paper proposes an intelligent intrusion detection system (IIDS) for AV-CPS using transfer learning to identify cyberattacks launched against connected physical components of AVs through a network infrastructure. First, AV-CPS was developed by implementing the controller area network (CAN) and integrating it into the AV simulation model. Second, the dataset was generated from the AV-CPS. The collected dataset was then preprocessed to be trained and tested via pre-trained CNNs. Third, eight pre-trained networks were implemented, namely, InceptionV3, ResNet-50, ShuffleNet, MobileNetV2, GoogLeNet, ResNet-18, SqueezeNet, and AlexNet. The performance of the implemented models was evaluated. According to the experimental evaluation results, GoogLeNet outperformed all other pre-rained networks, scoring an F1- score of 99.47%. [ABSTRACT FROM AUTHOR]

The last years have seen propagating Metaverse and Mixed Reality (Extended Reality, XR) technologies into everyday applications. Due to their immersion and digitalization capability, sustainability issues can be addressed to optimize resource consumption before processes are put to practice or products are materialized. In particular, Digital Process Twin technologies can execute behavior models of socio-technical Cyber-Physical Systems. They allow both designing variants of system behavior and validating implementation architectures for system operation. In this contribution, I leverage these capabilities to capture the behavior-centered intelligence of Metaverse and XR applications. The more accurately user roles and cyber-physical components can be captured by behavior models representing Metaverse/XR applications, the more accurately the environmental, social, and economic impact of design alternatives can be experienced. [ABSTRACT FROM AUTHOR]

Cyber‐physical systems (CPS) are applicable in emerging industries such as health, energy, autonomous vehicles, and Industrial Internet of Things (IIOT). In CPS the physical assets, that is, actuators and sensors, etc. communicate with each other over a messaging protocol. This communication process of CPS makes them vulnerable to cyber‐attacks which challenge the system safety, making security and safety two major concerns for CPS. Both safety and security risks are considered separately in literature. However, the International Society of Automation (ISA) focuses on the alignment of security and safety risks of CPS. Weak or no alignment in safety and security of a system may result in an inefficient or partially protected system which could end up in disasters. This pressed the need for an integrated safety‐security risk management process. For this purpose, we used a tetra packaging case study to (i) examine the vulnerabilities of CPS by running the risk management process, (ii) identify safety‐security requirements, and (iii) align retrieved safety‐security requirements with the relevant standards. The results show (i) safety hazards and security risks along with their severity and priority, (ii) mitigation guidelines are provided by consulting IEC 61508, and (iii) 15 safety‐security requirements are identified and are aligned with ISO9001 Packaging and labeling machine standard. [ABSTRACT FROM AUTHOR]

The Cyber Physical System (CPS) is a prime target for cyber attacks due to its heterogeneity and connectivity with physical equipment. This paper proposes a model based on a Hierarchical Bayesian Network (HBN) to increase the CPS’s attack detection ability. Denial of Service (DoS) attacks pose a significant threat to production line availability, business services, and human lives. Therefore, this paper focuses on detecting DoS attacks using the Bayesian network model, an efficient algorithm to detect faults in the networking system based on incomplete recognizing information. The standard Bayesian network model is hierarchically enhanced and optimized with a Bacterial Foraging Optimization (BFO) approach to improve the detection process. The developed, optimized model satisfies security, Quality of Service (QoS) requirements, and time consumption by reducing the constraints to obtain system reliability. The efficiency of the proposed model is evaluated using the NSL-KDD dataset and compared with existing approaches in terms of accuracy, precision, recall, F1-score, ROC, and RMSE. Compared to other existing systems, the proposed model achieves an accuracy of 98.4% in detecting DoS attacks with a reduced RMSE of 0.0617. [ABSTRACT FROM AUTHOR]

The convergence of cyber and physical systems through cyber–physical systems (CPSs) has been integrated into cyber–physical production systems (CPPSs), leading to a paradigm shift toward intelligent manufacturing. Despite the transformative benefits that CPPS provides, its increased connectivity exposes manufacturers to cyber-attacks through exploitable vulnerabilities. This paper presents a novel approach to CPPS security protection by leveraging digital twin (DT) technology to develop a comprehensive security model. This model enhances asset visibility and supports prioritization in mitigating vulnerable components through DT-based virtual tuning, providing quantitative assessment results for effective mitigation. Our proposed DT security model also serves as an advanced simulation environment, facilitating the evaluation of CPPS vulnerabilities across diverse attack scenarios without disrupting physical operations. The practicality and effectiveness of our approach are illustrated through its application in a human–robot collaborative assembly system, demonstrating the potential of DT technology.

This paper is written to propose a new architecture of the smart manufacturing system from the perspective of information system theory by synthesizing the achievements and experiences gained in the process of researching and developing the smart manufacturing system suitable for the situation of the Democratic People's Republic of Korea. Verifying the structure of a smart manufacturing system suitable for the situation of the Democratic People's Republic of Korea is an essential research task that presents the goals to be achieved in the R&D and introduction of smart manufacturing systems, which are put forward by different R&D groups. Based on a global study, synthesis, and analysis of the architectural features of smart manufacturing systems required by enterprises, the authors have identified a new structure of smart manufacturing system that meets the actual conditions of the Democratic People's Republic of Korea by verifying its rationality through the practical process of studying, developing, and constructing the smart manufacturing system in the digital book editing (virtual production) and printing (physical production) of a book printing factory. The findings are that (a) a smart manufacturing system is an informative and organic combination of management information systems and the internet of things with cyber-physical system as the core; (b) a product lifecycle management system is a comprehensive information integration tool of production and management related information systems to optimize the production and management activities of enterprises and put them on a high scientific and technological basis, and its implementation is essential; and (c) research on the smart manufacturing system should be continuously conducted to meet the demands of enterprise management practice that are constantly faced with new theoretical, practical, and information technology challenges, and such research results should be realized at a high level based on strong research, development, and introduction forces in the future. The finding of the new architecture of smart manufacturing system has been made in the context of the research on the smart manufacturing system, and it will serve as the theoretical basis for the smart manufacturing system to be built in various types of enterprises in the Democratic People's Republic of Korea. [ABSTRACT FROM AUTHOR]

As an indispensable part of the automobile factory transmission system, the transmission assembly line has a high level of automation and can play as a pioneer in the digital transformation of production. However, for the transmission production process, especially the front and middle case, there are many problems, such as a low degree of informatization, low efficiency of information transmission, and lack of a platform for rapid information release and sharing. This leads to the difficulty of timely discovery and feedback of exceptional events, resulting in low assembly efficiency. Therefore, this paper constructs its cyber–physical system (CPS) with the help of the Internet of Things (IoT) technologies, such as physical identification, information collection, and transmission. It contributes to improving the intelligent level of transmission of front and middle case assembly, strengthening the close integration, interaction, and collaboration between a large amount of assembly data and the physical entity of the assembly process, realizing the real-time monitoring of the transmission front and middle case assembly process, timely discovering, provide feedback, and dealing with exceptional events. In addition, scientific and effective adjustment strategies based on the data can be provided. Ultimately, the assembly efficiency of the transmission front and middle case is improved, ensuring the stability of the assembly line. [ABSTRACT FROM AUTHOR]

The integrated production manufacturing system based on cyber-physical system (CPS) is a complex production system that integrates perception, calculation, and control. Studying the multi-state reliability of integrated production manufacturing system based on CPS has the certain theory and practical significance. This paper presented a novel T-S fuzzy fault tree hybrid method to analyse its failure risk and reliability. Firstly, the fuzzy set theory was used to determine the state probability of the bottom event. Then the state probability and fuzzy possibility of the system were determined according to the state probability and actual state of the bottom event, respectively. Finally, the weak reliability links under different failure states of the system were determined according to the importance and sensitivity analysis. The application case analysis shows that the presented method is consistent with the field situation and can provide reference for failure risk and reliability analysis in integrated production manufacturing system based on CPS. [ABSTRACT FROM AUTHOR]

With the continuous expansion of air traffic flow, the increasingly serious aviation network security threats have a significant and far-reaching impact on the safe operation of the aviation industry. The networked air traffic management (ATM) system is an integrated space–air–ground network integrating communication, network, satellite, and data chain technology, which adopts a network-centric structure to provide network-enabled services and applications for the air transportation system. In view of the serious network threats faced by networked ATM, this paper studies the basic theories and key technologies of ATM information security assurance and designs a credible security architecture to provide comprehensive and systematic security assurance for networked ATM. Starting from the problems and development trend of ATM security assurance, this paper investigates the dynamic and complex data processing process of ATM system, analyzes the complex interaction between its cyber and physical system, and then constructs the networked ATM cyber–physical fusion system model and threat model. Furthermore, the causal relationship between ATM security threat alert information is mapped into a Bayesian network, and the game model of networked ATM is proposed using Bayesian Nash equilibrium strategy. At last, the information security assurance architecture of networked ATM system based on blockchain technology is formed by establishing a distributed co-trust mechanism and multi-chain storage structure. We expect this paper to bring some inspiration to the related research in academia and aviation so as to provide useful reference for the construction of ATM safety and security system and the development of technology. [ABSTRACT FROM AUTHOR]

Trust is the currency of every transaction and exchange and is the pillar of the trusted system concept, which is one of the needs of today and the future. Those systems ease communication and sharing with little user-side load and are used in numerous organizations, financial institutions, military scenarios, and highly confidential works. The evolution of Cyber-Physical Systems (CPS) affects people's way of life and is applied in health care, smart homes, commerce, etc. The cyber-physical system is treated as a trust system if the principles of security and safety, confidentiality, integrity, availability, and another set of properties are guaranteed. The development of CPS requires consistency in requirements management, metrics, formal test process descriptions, and computation methods. The research community is focused on how to realize a novel CPS with high confidence. In the literature, there is no clear definition of all kinds of trustworthiness metrics, and there is no classification of trustworthiness and trust metric types. There is no defined standard for trustworthiness, and there are no rules for calculating CPS trustworthiness. This paper proposes a framework for the evaluation of trust in CPS. This framework ranks the trustworthiness of CPS by degree. Trust degrees for the cyberphysical system are defined, along with a set of requirements and properties for each degree. We proposed a proprieties classification based on functionality and obligation as well as a simple mathematical formula to compute trust in the CPS, which formulates a quantitative view on the guarantee of security, trustworthiness, and trust attributes. The results of this study, which are based on the use of the proposed framework to evaluate the trust of CPS and case study, indicate that our quantitative method is more objective than existing qualitative methods. [ABSTRACT FROM AUTHOR]

With the increase in low-power wireless communication solutions, the deployment of Wireless Sensor Networks is becoming usual, especially to implement Cyber-Physical Systems. These latter can be used for Structural Health Monitoring applications in critical environments. To ensure a long-term deployment, battery-free and energy-autonomous wireless sensors are designed and can be powered by ambient energy harvesting or Wireless Power Transfer. Because of the criticality of the applications and the limited resources of the nodes, the security is generally relegated to the background, which leads to vulnerabilities in the entire system. In this paper, a security analysis based on an example: the implementation of a communicating reinforced concrete using a network of battery-free nodes; is presented. First, the employed wireless communication protocols are presented in regard of their native security features, main vulnerabilities, and most usual attacks. Then, the security analysis is carried out for the targeted implementation, especially by defining the main hypothesis of the attack and its consequences. Finally, solutions to secure the data and the network are compared. From a global point-of-view, this security analysis must be initiated from the project definition and must be continued throughout the deployment to allow the use of adapted, updatable and upgradable solutions. [ABSTRACT FROM AUTHOR]

The rapid growth of industry and the economy has contributed to a tremendous increase in traffic in all urban areas. People face the problem of traffic congestion frequently in their day-to-day life. To alleviate congestion and provide traffic guidance and control, several types of research have been carried out in the past to develop suitable computational models for short- and long-term traffic. This study developed an effective multi-dimensional dataset-based model in cyber–physical systems for more accurate traffic-volume prediction. The integration of quantum convolutional neural network and Bayesian optimization (QCNN_BaOpt) constituted the proposed model in this study. Furthermore, optimal tuning of hyperparameters was carried out using Bayesian optimization. The constructed model was evaluated using the US accident dataset records available in Kaggle, which comprise 1.5 million records. The dataset consists of 47 attributes describing spatial and temporal behavior, accidents, and weather characteristics. The efficiency of the proposed model was evaluated by calculating various metrics. The performance of the proposed model was assessed as having an accuracy of 99.3%. Furthermore, the proposed model was compared against the existing state-of-the-art models to demonstrate its superiority. [ABSTRACT FROM AUTHOR]

Threats of cyberattacks have penetrated from disclosing critical user information to destroying/manipulating industrial control systems. Study on data security during network transmission has raised increasing attention in the systems and control community, which is found very necessary and timely in the context of Industry 4.0. In most existing approaches, the protection of the transmitted data from eavesdropping attacks and the detection of malicious integrity attacks are usually carried out separately. In this study, an integrated data-driven framework applicable at the control level is proposed to deal with secure transmission and attack detection simultaneously. In the framework, a secure correlation-based encryption/decryption approach and a trustworthiness judgement approach are proposed. Comprehensive discussions are made regarding the analysis of the sensitivity to attacks, the introduced time delay, and the design degree-of-free. Executable algorithms are presented, corresponding to which hardware is modularized and can work standalone independent from the configuration of the monitoring and control systems or any third-party authentication agencies. Evaluation results on a simulated two-area frequency-load control power grid system are provided to show the effectiveness and performance of the proposed approaches. [ABSTRACT FROM AUTHOR]