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This paper focuses on a detection‐based resilient control issue for cyber‐physical systems (CPSs) subject to false data injection (FDI) attacks, where FDI attacks occur in the communication channels from the sensor‐to‐controller and controller‐to‐actuator. Firstly, to reduce the adverse impacts of FDI attacks on estimation performance, an unbiased estimator is constructed by tackling the equality‐constrained optimization problem. Then, an effective attack detection mechanism is devised by introducing a pseudo‐innovation sequence to formulate the detection function, which can successfully detect two‐channel FDI attacks. Based on these detection results, a resilient controller combining linear quadratic Gaussian and H∞$$ {H}_{\infty } $$ controllers is provided to guarantee the mean‐square asymptotic stability of CPSs with H∞$$ {H}_{\infty } $$ performance. Finally, the validity of the proposed resilient control approach is demonstrated by a simulation involving satellite control system. [ABSTRACT FROM AUTHOR]

ABSTRACT The main thrust of this study is to scrutinize the issues of fault estimation and asynchronous sampled‐data fault‐tolerant control for semi‐Markov jump cyber‐physical systems with external disturbances, faults and deception attacks. To do so, initially, an intermediate variable is framed and then using that variable as a foundation, a mode‐dependent intermediate estimator is constructed, which estimates the fault signals and system's state simultaneously. Due to the unavailability of mode information in the Markov chain for the observer/controller, a hidden Markov model is employed to represent the asynchronous scenario between the mode of the original system and that of the designed observer/controller. Subsequently, benefited by the estimated terms and sampled‐data approach, an asynchronous sampled fault‐tolerant control protocol is offered up that facilitates compensating for the faults occurring in the system. In the meantime, the extended passive performance is used to lessen the negative impact of external disturbances exerting on the system. Besides this, the deception attacks occurring in the system are presumed to have a stochastic nature that adheres to the Bernoulli distribution. Moreover, by constructing mode‐dependent Lyapunov–Krasovskii functional and blending it with integral inequalities, the sufficient condition confirming the intended outcomes is procured in the framework of linear matrix inequalities. Thereafter, on the platform of deduced adequate criteria, an explicit formulation for the requisite gain values can be obtained. Ultimately, simulation results are offered to verify the reliability of presented outcomes. [ABSTRACT FROM AUTHOR]

Due to the strive towards miniaturized systems and the growing field of optical technologies, micro-assembly is becoming increasingly important. Micro-assembly is characterized by challenging processes that require sub-micron level positioning accuracy regardless of modeling and calibration errors in the manipulator system. Automating these processes requires not only profound expertise about the process itself but also highly skilled personnel for programming the micro-assembly robot since current interfaces lack intuitive programming methods and simulation capabilities. In this paper, we outline a roadmap towards autonomous programming by combining intuitive programming approaches with intelligent and self-learning algorithms. Following this roadmap, the user will be supported progressively by autonomous and intelligent sub-processes until the machine can finally program itself autonomously. Based on a systematic review of the current state of automated micro-assembly and simulation frameworks, we show the capabilities of current approaches and identify key enablers for an autonomous assembly. From these enablers, we derive modules building our proposed framework. Central aspects are the development of a holistic simulation and a data management, which include not only the robot with its sensor systems but also assembly-components. These form the foundation for offline programming and the usage of machine learning algorithms. In order to facilitate future research, we propose the utilization of the Robot Operating System 2 framework (ROS2) as a basis for autonomous programming adhering the principles of open-source and enabling seamless integration. [ABSTRACT FROM AUTHOR]

Cyber–physical systems (CPSs) are evolving from individual systems to collectives of systems that collaborate to achieve highly complex goals, realizing a cyber–physical system of systems (CPSoSs) approach. They are heterogeneous systems comprising various autonomous CPSs, each with unique performance capabilities, priorities, and pursued goals. In practice, there are significant challenges in the applicability and usability of CPSoSs that need to be addressed. The decentralization of CPSoSs assigns tasks to individual CPSs within the system of systems. All CPSs should harmonically pursue system-based achievements and collaborate to make system-of-system-based decisions and implement the CPSoS functionality. The automotive domain is transitioning to the system of systems approach, aiming to provide a series of emergent functionalities like traffic management, collaborative car fleet management, or large-scale automotive adaptation to the physical environment, thus providing significant environmental benefits and achieving significant societal impact. Similarly, large infrastructure domains are evolving into global, highly integrated cyber–physical systems of systems, covering all parts of the value chain. This survey provides a comprehensive review of current best practices in connected cyber–physical systems and investigates a dual-layer architecture entailing perception and behavioral components. The presented perception layer entails object detection, cooperative scene analysis, cooperative localization and path planning, and human-centric perception. The behavioral layer focuses on human-in-the-loop (HITL)-centric decision making and control, where the output of the perception layer assists the human operator in making decisions while monitoring the operator's state. Finally, an extended overview of digital twin (DT) paradigms is provided so as to simulate, realize, and optimize large-scale CPSoS ecosystems. [ABSTRACT FROM AUTHOR]

With the development of the Internet of Things (IoT) in power distribution and the advancement of energy information integration technologies, the explosive growth in network data volume caused by massive terminal devices connecting to the power distribution network has become a significant challenge. Multi‐terminal collaborative computing is a key approach to addressing issues such as high latency and high energy consumption. In this article, fog computing is introduced into the computing network of the power distribution system, and a cloud‐fog‐edge collaborative computing architecture for intelligent power distribution networks is proposed. Within this framework, an improved weighted K‐means method based on information entropy theory is presented for node partitioning. Subsequently, an improved multi‐objective particle swarm optimization algorithm (MWM‐MOPSO) is employed to solve the task resource allocation problem. Finally, the effectiveness of the proposed architecture and allocation strategy is validated through simulations on the OPNET and PureEdgeSim platforms. The results demonstrate that, compared to traditional cloud‐edge service architectures, the proposed architecture and task offloading scheme achieve better performance in terms of processing latency and energy consumption. [ABSTRACT FROM AUTHOR]

Cyber‐physical Systems (CPSs) are characterized by entities in both the physical and the virtual space, thus enabling an immersion of the physical world into the cyberspace. Connectivity via the cyberspace allows CPS cooperation for new services in product service systems (PSS). In consequence, cooperating CPSs act as actors with interest in the CPS in focus. Considering the needs of human actors and cooperating CPSs is a challenging task in CPS development because of many actors, interdepending CPS functions, and multiple CPS interfaces. For systems, the known Functional Architectures for Systems (FAS) method offers a solution approach for deriving functional system architectures from system use cases originating from human actors. For CPS development, this publication presents an expansion of the FAS method for developing functional architectures based on use cases originating from human actors as well as from cooperating CPSs and offers a model‐based approach based on the method description. In the authors' opinion, interconnectable CPSs and models of cooperating CPSs can be integrated and interconnected with each other into a unifying aggregated model to represent the joint behavior of CPSs in an aggregated system. The paper explains this novel approach through a CPS functional architecture development example related to the prediction of remaining boarding time in an aircraft. The result is an approach that allows the consideration of initial CPS functions and new aggregated system functions, that pays special attention to the interconnectivity of CPSs and the required interfaces, and enables the systematic analysis of functions for the identification of redundancies. [ABSTRACT FROM AUTHOR]

This paper addresses the issue of frequency recovery in distributed battery energy storage systems (BESSs) and the balancing of the state of charge (SOC) after secondary control inputs have been subjected to false data injection attacks (FDI). A fuzzy high order differentiator (FHOD) observer based distributed resilient control is introduced with the aim of achieving frequency restoration and power adaptive allocation. In order to mitigate the performance degradation caused by the chattering of traditional sliding mode observers, a high order differentiator (HOD) is employed as an observer. By introducing fuzzy control logic to optimize the coefficients of the differentiator, the coefficient of the proposed differentiator is adjusted based on the tracking error, thereby reducing the transient overshoot of the observer when the attack signal changes, which approach effectively balances the response speed and observation accuracy. The simulation outcomes demonstrate that the suggested control strategy is able to mitigate the effects caused by complex and unbounded FDI attacks on the BESSs and exhibits superior transient performance compared to traditional HOD observer in response to changes in attack signal. [ABSTRACT FROM AUTHOR]

In this paper, an adaptive event‐triggered control scheme is proposed for a class of continuous‐time linear cyber‐physical systems (CPSs) with unknown false data injection attacks (FDIA) and state constraints. First, the adaptive boundary estimation mechanism and Nussbaum‐type function are introduced into the two‐step backstepping control, which successfully reduces the impact of unknown attack gain. Second, considering that the Nussbaum function causes the chattering problem of the system state, the Tangent Barrier Lyapunov function (TBLF) is used to constrain the state. Then, the controller is designed in combination with the event‐triggered mechanism (ETM), which saves communication resources. Based on the designed adaptive event‐triggered security control method, all signals of the closed‐loop system can be guaranteed to be bounded, and the state constraints are not violated. Finally, the simulation results verify the effectiveness and rationality of the proposed control strategy. [ABSTRACT FROM AUTHOR]

The rise of suspicious activities in network communication, driven by increased internet accessibility, necessitates the development of advanced intrusion detection systems (IDS). Existing IDS solutions often exhibit poor performance in detecting suspicious activity and fail to identify various attack types within packet capture (PCAP) files, which monitor network traffic. This paper proposes a deep learning‐based dual IDS model designed to address these issues. The process begins with utilizing the CSE‐CIC‐IDS2019 dataset to extract features from PCAP files. Suspicious activities are detected using the Exponential Geometric‐Gaussian Error Linear Units‐Gated Recurrent Unit (EG‐GELU‐GRU) method. Normal data undergoes further feature extraction and preprocessing through Log ZScore‐Jacosine Density‐Based Spatial Clustering of Applications with Noise (LZ‐JC‐DBSCAN). Feature selection is optimized using the Entropy Pearson R Correlation‐Red Panda optimization algorithm. Suspicious files are flagged, while load balancing is performed on normal data. Attack detection is achieved through word embedding with the Glorot Kaufman‐bidirectional encoder representations from transformers technique and classification via the EG‐GELU‐GRU model. Attacked packets are blocked, and the method is reapplied for attack‐type classification. Experimental results using Python demonstrate the model's superior performance, achieving 98.18% accuracy and 98.73% precision, surpassing existing approaches and significantly enhancing intrusion detection capabilities. [ABSTRACT FROM AUTHOR]

The architecture, engineering, and construction (AEC) industry in the US faces increasing labor shortages while accumulating downward productivity trends. Previous research efforts have shown that lack of timely access to information is one of the main factors hindering construction workers' productivity, and prompt information extraction (IE) from BIM sources can help address this issue. This study develops a BIM-web interoperable system, featuring a voice-based intelligent virtual agent (VIVA), to provide workers with information on-demand when conducting their activities on the jobsite. VIVA integrates construction digital sources with web platforms to enable real-time data communication between the users, workers in this case, and BIM platforms. The voice interaction feature of VIVA provides an intuitive and safe tool that can easily be used by workers with varying ranges of experience, with minimal need for training. The responsiveness and accuracy of VIVA is evaluated through two proof-of-concept case studies. Results indicate that the BIM-VIVA link and underlying natural language understanding (NLU) algorithms in the Google Actions platform accurately understand user queries, with a performance of 97.50% correct responses. As a result, VIVA achieves superior performance compared with previous research works based on NLU algorithms with text-based queries. The developments made in this research not only contribute to improving the safety and productivity of construction workers but also can open the door for easier onboarding of less-experienced workers, thus addressing the industry's severe skilled labor shortage issue. Practical Applications: This study introduces an innovative web-based system featuring VIVA to address the pressing labor shortage and diminishing productivity in the AEC industry. VIVA seamlessly integrates construction digital sources with web platforms, enabling real-time communication with construction workers and facilitating their on-demand information retrieval. The intuitive voice interaction feature of VIVA offers a safe and easily navigable tool that demands minimal training for workers with diverse backgrounds and experience levels. This feature also makes VIVA a practical tool for hands-free communication and information retrieval on jobsites, which minimizes the imposed distraction inherent in technological solutions. Construction workers can use voice commands to access project plans, schedules, and safety protocols, allowing them to retrieve critical information without having to stop work or use their hands. This intuitive interface reduces the need for complex user interfaces or manual input, making it easier for workers to interact with technology while focusing on their tasks. Hence, VIVA promises to improve productivity and safety of diverse group of workers without causing distraction or safety issues itself, which is a common concern when implementing technology to assist workers in hazardous and ever-changing construction jobsites. Beyond enhancing safety and productivity, widespread use of VIVA can help address the AEC sector's severe skilled labor shortage by providing an accessible means to onboard less-experienced workers. As such, VIVA provides opportunities to augment efficiency for seasonal/nonnative workers, thus facilitating the integration of a broader workforce and addressing the industry's pressing labor challenges. [ABSTRACT FROM AUTHOR]

This paper primarily focuses on designing stealthy false data injection attacks targeting two communication channels in cyber‐physical systems equipped with state estimators and attack detectors. It introduces the concept of perfect attacks, rendering the attack detector unable to detect the designed attack signals, thereby enabling attackers to further destabilize the system. Through the error model designed by the attacker, the designed attack does not require knowledge of the state estimator. By injecting a carefully designed perfect attack into the communication channel, it is possible to extend the state estimation error of a residual detector system based on the Kalman filter to infinity. Moreover, this designed attack can bypass the residual‐based detector, thus achieving a completely stealthy attack. Finally, the results of theoretical derivations are validated by a numerical example and a practical case. [ABSTRACT FROM AUTHOR]

This paper investigates the stabilization problem for cyber‐physical systems subject to positive constraint and denial‐of‐service (DoS) attacks using the event‐triggered scheme. Unlike traditional modeling of cyber physical systems with DoS attack, this paper establishes a positive switched system model composed of two positive subsystems that according to the DoS attack is active or inactive. Considering the effect of positive constraint and DoS attack, a 1‐norm‐based event‐triggered mechanism is proposed in the sleep interval of DoS attacks. By incorporating switching theory with event‐triggered mechanism, the sufficient condition of positive property and exponential stabilization for the considered system is established. Thereafter, the gain matrix of the controller is calculated by matrix decomposition technique. Furthermore, the Zeno behavior is eliminated with a lower bound of the event‐triggered interval. Finally, an example is carried out to verify the validity of the theoretical results. [ABSTRACT FROM AUTHOR]

False data injection (FDI) attacks have significantly threatened the security of the cyberphysical system (CPS). To ensure the stability of the CPS under FDI attacks, resilient model predictive control (MPC) has been extensively researched. However, most of the existing resilient MPC methods fail to consider the network resource limitation of the CPS, which may not be applicable in some real systems. Therefore, in this article, a resilient self-triggered (ST) MPC strategy is developed for a discrete-time nonlinear CPS under FDI attacks, which can not only ensure the system’s stability but also reduce resource consumption via decreasing the update frequency of MPC and economizing the utilization of the protection resource. First, an input signal reconstruction mechanism is designed based on key control data selection, which could reconstruct a feasible control sequence for the CPS even if the original ST control sequence is tampered with by FDI attacks. Then, a resilient ST-MPC algorithm based on the input signal reconstruction mechanism is proposed to weaken the adverse effects of FDI attacks and reduce resource consumption simultaneously. Moreover, the recursive feasibility of the resilient ST-MPC mechanism and input-to-state stability of the controlled system are respectively proved. Finally, the performance of the resilient ST-MPC mechanism is shown through a cart–damper–string system and an intelligent vehicle system. [ABSTRACT FROM AUTHOR]

ABSTRACT This article studies a stealthy attack strategy design for distributed state estimation from the perspective of an attacker in cyber‐physical systems, where both internal and external attack scenarios are considered simultaneously. To enhance robustness against outliers and reduce communication burden, a distributed fusion estimator is developed by fusing the innovation residuals of neighboring smart sensor nodes without any attack. Based on the designed distributed fusion estimator, the evolution of the distributed estimation error covariance is analyzed, and its lower and upper bounds are obtained. Moreover, a stealthy attack framework embedding adjustable parameter is designed to weaken the estimation performance, where the constraints of the adjustable parameter are provided based on the desired attack effect. Finally, a simulation example is provided to manifest the validity of the main results. [ABSTRACT FROM AUTHOR]

A mini-review of the literature, supporting the view on the psychophysical origins of some user acceptance effects of cyber-physical systems (CPSs), is presented and discussed in this paper. Psychophysics implies the existence of a lawful functional dependence between some aspect/dimension of the stimulation from the environment, entering the senses of the human, and the psychological effect that is being produced by this stimulation, as reflected in the subjective responses. Several psychophysical models are discussed in this mini-review, aiming to support the view that the observed effects of reactance to a robot or the uncanny valley phenomenon are essentially the same subjective effects of different intensity. Justification is provided that human responses to technologically and socially ambiguous stimuli obey some regularity, which can be considered a lawful dependence in a psychophysical sense. The main conclusion is based on the evidence that psychophysics can provide useful and helpful, as well as parsimonious, design recommendations for scenarios with CPSs for social applications. [ABSTRACT FROM AUTHOR]

Communication routing of cyber‐physical power systems (CPPS) with the high penetration of renewable energy sources (RES) plays an important role in the resilience enhancement against disasters, natural and man‐made alike. Therefore, a trilevel optimization model is proposed for the CPPS resilience enhancement with the RES against extreme events. The upper‐level model identifies optimal hardening lines of both the transmission and communication networks with consideration of the communication routing constraints. The middle‐level model identifies the attacked lines to maximize load shedding of the power system. The effects of RES uncertainty and communication routings on resilience enhancement are analysed. The lower‐level model attains an optimal allocation strategy of power generation to minimize load shedding. The model is solved by the column‐and‐constraint generation algorithm. Case studies are conducted on the IEEE 14‐bus, RTS 24‐bus, and 118‐bus system. The results show that the proposed hardening strategy can effectively ensure the adaptiveness of the transmission network and communication routing to improve the resilience of CPPS to extreme events. Moreover, it is observed that the load loss and total investment cost are heavily affected without and with RES. [ABSTRACT FROM AUTHOR]

In this work, we target the efficient implementation of spiking neural networks (SNNs) for low-power and low-latency applications. In particular, we propose a methodology for tuning SNN spiking activity with the objective of reducing computation cycles and energy consumption. We performed an analysis to devise key hyper-parameters, and then we show the results of tuning such parameters to obtain a low-latency and low-energy embedded LSNN (eLSNN) implementation. We demonstrate that it is possible to adapt the firing rate so that the samples belonging to the most frequent class are processed with less spikes. We implemented the eLSNN on a microcontroller-based sensor node and we evaluated its performance and energy consumption using a structural health monitoring application processing a stream of vibrations for damage detection (i.e. binary classification). We obtained a cycle count reduction of 25% and an energy reduction of 22% with respect to a baseline implementation. We also demonstrate that our methodology is applicable to a multi-class scenario, showing that we can reduce spiking activity between 68 and 85% at iso-accuracy. [ABSTRACT FROM AUTHOR]

As real-time data sources expand, the need for detecting anomalies in streaming data becomes increasingly critical for cutting edge data-driven applications. Real-time anomaly detection faces various challenges, requiring automated systems that adapt continuously to evolving data patterns due to the impracticality of human intervention. This study focuses on energy systems (ES), critical infrastructures vulnerable to disruptions from natural disasters, cyber attacks, equipment failures, or human errors, leading to power outages, financial losses, and risks to other sectors. Early anomaly detection ensures energy supply continuity, minimizing disruption impacts, an enhancing system resilience against cyber threats. A systematic literature review (SLR) is conducted to answer 5 essential research questions in anomaly detection due to the lack of standardized knowledge and the rapid evolution of emerging technologies replacing conventional methods. A detailed review of selected literature, extracting insights and synthesizing results has been conducted in order to explore anomaly types that can be detected using Machine Learning algorithms in the scope of Energy Systems, the factors influencing this detection success, the deployment algorithms and security measurement to take in to consideration. This paper provides a comprehensive review and listing of advanced machine learning models, methods to enhance detection performance, methodologies, tools, and enabling technologies for real-time implementation. Furthermore, the study outlines future research directions to improve anomaly detection in smart energy systems. [ABSTRACT FROM AUTHOR]

This paper investigates the sliding mode control problem for cyber‐physical systems subject to multi‐source disturbances and actuator false data injection attacks simultaneously. Firstly, a series of extended state observers are designed to estimate the unknown multi‐source disturbances. For the actuator false data injection attacks, a sliding mode observer is designed to online estimate the values of attacks. Then, by introducing the disturbance and attack estimations, a dynamic sliding manifold and a composite sliding mode controller are constructed. Under the proposed approach, the unknown disturbances and false data injection attacks can be timely estimated and compensated, which can improve the disturbance rejection ability and guarantee security of the cyber‐physical system. Lyapunov theory is utilized to prove the stability of the closed‐loop system. Finally, simulations of both the numerical example and application to a power converter system demonstrate the effectiveness and superiority of the proposed composite sliding mode control approach. [ABSTRACT FROM AUTHOR]

In this article, we propose a data‐driven networked control architecture for unknown and constrained cyber‐physical systems capable of detecting networked false‐data‐injection attacks and ensuring plant's safety. In particular, on the controller's side, we design a novel robust anomaly detector that can discover the presence of network attacks using a data‐driven outer approximation of the expected robust one‐step reachable set. On the other hand, on the plant's side, we design a data‐driven safety verification module, which resorts to worst‐case arguments to determine if the received control input is safe for the plant's evolution. Whenever necessary, the same module is in charge of replacing the networked controller with a local data‐driven set‐theoretic model predictive controller, whose objective is to keep the plant's trajectory in a pre‐established safe configuration until an attack‐free condition is recovered. Numerical simulations involving a two‐tank water system illustrate the features and capabilities of the proposed control architecture. [ABSTRACT FROM AUTHOR]

Summary: In the era of digitalization boom, cyber‐physical system (CPS) has been widely used in several fields. However, malicious data tampering in communication networks may lead to degradation of the state estimation performance, which may affect the control decision and cause significant losses. In this paper, for the identification of finite impluse response (FIR) systems with binary‐valued observations under data tampering attack, an optimal attack strategy based on the average entropy is designed from the perspective of the attacker. In the case of unknown parameters, the regression matrix is used to give the estimation method of the system parameters, the algorithmic flow of the data tampering attack for the implementation of the on‐line attack is designed. Finally, the effectiveness of the algorithm and the reliability of the conclusions is verified through the examples. [ABSTRACT FROM AUTHOR]

Autonomous underwater vehicles (AUV) constitute a specific type of cyber-physical system that utilize electronic, mechanical, and software components. A component-based approach can address the development complexities of these systems through composable and reusable components and their integration, simplifying the development process and contributing to a more systematic, disciplined, and measurable engineering approach. In this article, we propose an architecture to design and describe the optimal performance of components for an AUV engineering process. The architecture involves a computing approach that carries out the automatic control of a testbed using genetic algorithms, where components undergo a 'physical-running' evaluation. The procedure, defined from a method engineering perspective, complements the proposed architecture by demonstrating its application. We conducted an experiment to determine the optimal operating modes of an AUV thruster with a flexible propeller using the proposed method. The results indicate that it is feasible to design and assess physical components directly using genetic algorithms in real-world settings, dispensing with the corresponding computational model and associated engineering stages for obtaining an optimized and tested operational scope. Furthermore, we have developed a cost-based model to illustrate that designing an AUV from a physical-running perspective encompasses extensive feasibility zones, where it proves to be more cost-effective than an approach based on simulation. [ABSTRACT FROM AUTHOR]

In this research, we investigate the integration of an Intrusion Detection System (IDS) with a Digital Twin (DT) to enhance the cybersecurity of physical devices in cyber–physical systems. Using Eclipse Ditto as the DT platform and Snort as the IDS, we developed a near-realistic test environment that included a Raspberry Pi as the physical device and a Kali Linux virtual machine to perform common cyberattacks such as Hping3 flood attacks and NMAP reconnaissance scans. The results demonstrated that the IDS effectively detected Hping3-based flood attacks but showed limitations in identifying NMAP scans, suggesting areas for IDS configuration improvements. Furthermore, the study uncovered significant system resource impacts, including high Central Processing Unit (CPU) usage during SYN and ACK flood attacks and persistent memory usage after Network Mapper (NMAP) scans, highlighting the need for enhanced recovery mechanisms. This research presents a novel approach by coupling a Digital Twin with an IDS, enabling real-time monitoring and providing a dual perspective on both system performance and security. The integration offers a holistic method for identifying vulnerabilities and understanding resource impacts during cyberattacks. The work contributes new insights into the use of Digital Twins for cybersecurity and paves the way for further research into automated defense mechanisms, real-world validation of the proposed model, and the incorporation of additional attack scenarios. The results suggest that this combined approach holds significant promise for enhancing the security and resilience of IoT devices and other cyber–physical systems. [ABSTRACT FROM AUTHOR]

The cyber–physical systems (CPSs) of various stakeholders from the mobility, logistics, and security sectors are needed to enable smart and secure situational awareness operations in a port environment. The motivation for this research arises from the challenges caused by some unexpected events, such as accidents, in such a multi-stakeholder critical environment. Due to the scale, complexity, and cost and safety challenges, a simulation-based approach was selected as the basis for the study. Prototype-level experimental solutions for dataspaces for secure data sharing and visualization of situational awareness were developed. The secure data-sharing solution relies on the application of verifiable credentials (VCs) to ensure that data consumers have the required access rights to the data/information shared by the data prosumer. A 3D virtual digital twin model is applied for visualizing situational awareness for people in the port. The solutions were evaluated in a simulation-based execution of an accident scenario where a forklift catches fire while loading a docked ship in a port environment. The simulation-based approach and the provided solutions proved to be practical and enabled the smooth study of disaster-type situations. The realized concept of dataspaces is successfully applied here for both daily routine operations and information sharing during accidents in the simulation-based environment. During the evaluation, needs for future research related to perception, comprehension, projection, trust, and security as well as performance and quality of experience were detected. Especially, distributed and secure viewpoints of objects and stakeholders toward real-time situational awareness seem to require further studies. [ABSTRACT FROM AUTHOR]

The optimised design, operation and management of complex, large-size Cyber Physical Systems (CPSs) – like modern manufacturing and logistic assets – calls for Digital Twins (DTs) in which dynamic modelling and simulation play a relevant role. In such simulation-based DTs computational efficiency is crucial. According to the present state of the art, the said efficiency is hindered by the way the Cyber part of a CPS is represented, which in the manufacturing case practically corresponds to representing digital controls. This paper proposes a modelling framework for an efficient and scalable representation of the Cyber part in a DT of a CPS. The presented solution is based on Object-Oriented Modelling (OOM) and IEC industrial standards. Motivating and explanatory examples are provided, and a proof-of-concept case study is discussed to support the framework potential and industrial viability. [ABSTRACT FROM AUTHOR]

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In recent years, the rapid advancement of digital technologies has driven a profound transformation in both individual lives and business operations. The integration of Industry 4.0 with advanced mechatronic systems is at the forefront of this digital transformation, reshaping the landscape of smart manufacturing. This article explores the convergence of digital technologies and physical systems, with a focus on the critical role of mechatronics in enabling this transformation. Using technologies such as advanced robotics, the Internet of Things (IoT), artificial intelligence (AI), and big data analytics, industries are developing intelligent and interconnected systems capable of real-time data exchange, distributed decision making, and automation. The paper further explores two case studies: one on a smart plastic injection moulding machine and another on soft robots. These examples illustrate the synergies, benefits, challenges, and future potential of integrating mechatronics with Industry 4.0 technologies. Ultimately, this convergence fosters the development of smart factories and products, enhancing manufacturing efficiency, adaptability, and productivity, while also contributing to sustainability by reducing waste, optimising resource usage, and lowering the environmental impact of industrial production. This marks a significant shift in industrial production towards more sustainable practices. [ABSTRACT FROM AUTHOR]

Power electronic converters are widely implemented in many types of power applications such as microgrids. Power converters can make a physical connection between the power resources and the power application. To control a power converter, required data such as the voltage and the current of that should be measured to be used in a control application. Therefore, a communication-based structure including sensors and communication links can be used to measure the desired data and transmit that to the controllers. So, a power converter-based system can be considered as a type of cyber-physical system, and it can be vulnerable to cyber-attacks. Then, it can strongly be recommended to use a strategy for a power converter-based system to monitor the system and identify the existence of cyber-attack in the system. In this study, artificial intelligence (AI) is deployed to calculate the value of the false data (i.e., constant false data, and time-varying false data) and detect false data injection cyber-attacks on power converters. Besides, to have a precise technical evaluation of the proposed methodology, that is evaluated under other issues, i.e., noise, and communication link delay. In the case of noise, the proposed strategy is examined under noises with different signal-to-noise ratios. Further, for the case of the communication delay, the system is examined under both symmetrical (i.e., same communication delay on all inputs) and unsymmetrical communication delays (i.e., different communication delay/delays on the inputs). In this work, artificial neural networks are implemented as the AI-based application, and two types of the networks, i.e., feedforward (as a basic type) and long short-term memory (LSTM)-based network as a more complex network are tested. Finally, three important AI-based techniques (regression, classification, and clustering) are examined. Based on the obtained results, this work can properly identify and calculate the false data in the system. [ABSTRACT FROM AUTHOR]

This study develops an active security control strategy for Cyber-Physical Systems (CPSs) that are subject to attacks known as Denial-of-Service (DoS), which can target both channels from the controller to the actuator and from the controller to the sensor. Due to attack cost restrictions, the linked channels are subject to a limit on the number of continuous DoS attacks. A proactive security control method is then developed to combat two-channel DoS attacks, depending on a method for identifying IoT intrusions. Using the CICIDS dataset for attack detection, we examined the effectiveness of the Deep Convolutional Network Model (DCNM), a suggested deep learning model. The addressed CPS can be asymptotically stable against DoS assaults under the security controller's active security control technique without sacrificing control performance. Recent tests and simulations show how effective the security control strategy is active. The proposed model gives better trade-off compared to existing approaches like Deep Belief Networks (DBN), Recurrent Neural Networks (RNN), Support Vector Machines (SVM), Supervised Neural Networks (SNN) and Feed Forward Neural Networks (FNN). The proposed model gives 99.3%, 99.5%, 99.5%, 99.6%, 99%, 98.9%, 99% accuracy with normal attack detection, botnet attack detection, Brute force attack detection, DoS attack detection, Infiltration attack detection, Portscan attack detection and web attack detection respectively. [ABSTRACT FROM AUTHOR]

In an era dominated by the Internet of Things (IoT), protecting interconnected devices from botnets has become essential. This study introduces an innovative hybrid deep learning model that synergizes LSTM Auto Encoders and Multilayer Perceptrons in detecting botnets in IoTs. The fusion of these technologies facilitates the analysis of sequential data and pattern recognition, enabling the model to detect intricate botnet activities within IoT networks. The proposed model's performance was carefully evaluated on two large IoT traffic datasets, N-BAIoT2018 and UNSW-NB15, where it demonstrated exceptional accuracy of 99.77 % and 99.67 % respectively for botnet detection. These results not only demonstrate the model's superior performance over existing botnet detection systems but also highlight its potential as a robust solution for IoT network security. [ABSTRACT FROM AUTHOR]

This article investigates the robust consensus problem for nonlinear multiagent systems against time‐varying false data injection attacks on actuators and sensors. First, the root‐mean‐square (RMS) theory is used to extend the assumption of the slow‐varying or constant attack signals to the case of time‐varying attack signals. Second, a novel distributed multivariate observer (DMO) is designed to estimate the followers' system states and the time‐varying attack signals on actuators and sensors. With the help of the outputs of DMO, a distributed robust consensus control arithmetic is proposed, which can compensate for actuator attacks and isolate sensor attacks so that exponential consensus and robust consensus are achieved. In particular, the robust performance of estimation errors and consensus errors is ensured by establishing the RMS gain index via linear matrix inequality, in which the zero initial conditions of estimation errors and consensus errors are not required. Finally, two simulation examples, including a network of four aircraft longitudinal dynamic systems, are given to verify the effectiveness of the proposed arithmetic. [ABSTRACT FROM AUTHOR]

Cyber-Physical Systems (CPS) are complex systems that integrate information control devices with physical resources, which can be automatically and formalized verified by model checking according to the expected requirements in the formal specification. The counterexamples in model checking are witnesses to the violation of the specification properties of the system and can provide important diagnostic information for debugging, controlling, and synthesizing CPS. Designing a rational specification language for CPS and generating effective counterexamples allows security vulnerabilities to be detected and addressed early in the system development. However, CPS involve frequent interactions between cyber and physical systems and often operate in unreliable environments, which poses new challenges for comprehensive modeling and designing specification languages for CPSs with discrete, continuous, time, probabilistic, and concurrent behaviors. Moreover, finding the smallest counterexample of CPS with probabilistic behavior in the shortest possible time has been identified as a Non-Deterministic Polynomial-complete (NP-complete) problem. Although a number of heuristics have been devised to address this challenge, the accuracy and efficiency of the solved counterexamples need to be improved due to the difficulty in determining the heuristic functions. We first provide a comprehensive model for CPS by introducing the Hybrid Probabilistic Time Labeled Transition System (HPTLTS). Subsequently, we design a specification language HPTLTS Temporal Logic (HPTLTS-TL) that can describe the properties of CPS. In addition, we propose an optimization algorithm CACO-A, which combines the Ant Colony Optimization (ACO) algorithm and the A-algorithm to efficiently generate the counterexample of CPS, which is represented as the diagnostic subgraph. Finally, we discuss a typical CPS example to demonstrate the feasibility of our approach. [ABSTRACT FROM AUTHOR]

In this paper, the preview control problem for cyber–physical systems (CPSs) under denial-of-service (DOS) attacks is studied. First, we employ an attack-tolerant strategy to design an augmented error system (AES) for scenarios where both state and reference signal channels are subject to periodic attacks. We then discuss the stochastic stability conditions for the AES and derive the corresponding controller. Subsequently, the preview controller for the original system is developed. Finally, the effectiveness of the obtained results is demonstrated through a numerical simulation using an unmanned ground vehicle (UGV) model, indicating the practical applicability of the proposed control strategy. [ABSTRACT FROM AUTHOR]

This article focuses on the problem of stability for a class of linear networked control systems (NCSs) subjected to network communication delays and random deception attacks. A new memory event‐triggered mechanism (METM) is proposed to reduce the unnecessary transmitted data through the communication channel and then enhance the network resources. In this context, a new memory stochastic state feedback controller is proposed to stabilize the closed‐loop networked control system. A new randomly occurring deception attacks model is employed to deal with the security problem of NCSs. Sufficient stability conditions are derived based on a suitable Lyapunov‐Krasovskii functional (LKF). The designed methodology is proposed in terms of linear matrix inequality to synthesize both event‐triggered parameters and controller gains, and to reduce the conservatism of the system some integral lemma are exploited to bind the time derivative of the LKF. Finally, two numerical examples are presented to illustrate the effectiveness of the proposed method which provides a maximal upper bound value of the network‐induced delay and less transmitted packet regarding the maximal value delay obtained in other works, so less conservatism results are obtained, compared to previous ones in the literature. [ABSTRACT FROM AUTHOR]

Summary: This paper proposes a new detection, reconstruction and mitigation scheme for nonlinear cyber‐physical systems experiencing deception attacks in controller‐actuator channel. For early detection of attacks, an anomaly detection unit based on the diagnostic observer is designed. After residual generation and evaluation, attack is detected. Upon attack detection, an adaptive fuzzy wavelet network (FWN) as an online nonlinear estimator is activated to reconstruct the detected malicious attack. Then, attack mitigation mechanism based on the command filtered backstepping approach and reconstructed attack is activated to mitigate the adverse effect of the detected attack. Stability analysis of the suggested strategy is presented and simulation results are provided to show the effectiveness of the suggested scheme. [ABSTRACT FROM AUTHOR]

The deep integration of information technology and process industry production systems makes system failure increasingly multi‐source and multi‐scale. In contrast to conventional hazard methods, system theoretic process analysis (STPA) can analyze the hazards in system control processes from the perspective of interactions among the system components. Theoretically, this method offers advantages that are better suited for modern production systems. However, as of now, the integration between STPA and process industrial production systems is still lacking. To address this issue, this study improved the original STPA method. First, we propose the "5 flows" concept for the process industrial cyber‐physical systems. The systems are described using multilevel flow modeling (MFM). This leads to the development of the MSTPA method, which is specifically designed to analyze the cyber‐physical hazards in process industrial production systems. Subsequently, the cyber‐physical hazards of a fluidized‐bed catalytic cracking unit are analyzed in detail using the MSTPA method as an example. The results show that MSTPA can identify cyber‐physical hazards in multiple dimensions. It is proved that, compared with the original STPA and traditional hazard methods, the MSTPA method can better identify cyber‐physical hazards in process industrial production systems. [ABSTRACT FROM AUTHOR]

In this article, the hybrid‐triggered predictive control of cyber‐physical systems (CPSs) under cyber‐attacks is researched. Firstly, considering the unpredictable state of the CPSs, an observer‐based model is constructed, and the predictor is put forward to predict state. Secondly, a hybrid‐triggered scheme is adopted to avoid unnecessary‐data transmission by introducing a hybrid switch mode. Thirdly, the deception attacks and DoS attacks are existed in sensor‐to‐observer (S‐O) and observer‐to‐controller (O‐C), separately. Two types of attack modeling are established based on their characteristics. With the proposed control method, some sufficient conditions are obtained to ensure the input‐to‐state stability (ISS) of CPSs. Finally, the results of simulation examples are received to testify the advantage of developed control strategy. [ABSTRACT FROM AUTHOR]

Cyber‐physical systems (CPSs) are increasingly vulnerable to the threat from malicious adversaries owing to the deep integration of networks and facilities; therefore, CPSs' security against cyber attacks has attracted extensive attention. The deception attack is one of the typical cyber attacks targeting CPSs, and a deception attack mode is established to describe the simultaneous occurrence of actuator and sensor attacks in this paper. Compared with single‐target deception attacks with constant value, the designed attack mode that can describe deception attacks against multiple components and embedded random characteristics has higher applicability in practical engineering processes. To counter such attack behavior, a secure estimator is proposed based on the ideology of augmented state to achieve the optimal design of secure state estimation and deception attack identification. Taking the energy shortage problem of CPSs into account, a novel event‐triggered communication mechanism is introduced on the basis of measurement residuals to improve resource efficiency by reducing low‐value information transmission. The communication scheme and the augmented state estimator are co‐designed by adopting the implicit information of the event‐triggered mechanism. Then, the performance of the co‐designed estimator is analyzed by the upper bound of the estimation error covariance matrix. Besides, the reconciliation between estimation performance and communication energy consumption is achieved by the derivation of Pareto equilibrium. Finally, applications of our approaches to a 3D target tracking system are provided to illustrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]