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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]

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]

This study explores the security control challenges encountered in cyber-physical systems when facing unknown nonlinearities and network attacks. It adopts an approach integrating observer-based adaptive sliding mode control and a threshold-changing event-triggered mechanism. Initially, a sliding mode function is formulated utilizing observer state, leading to the derivation of error dynamics and sliding mode dynamics. Subsequently, conditions ensuring stability of the closed-loop system are established through Lyapunov theory, expressed in terms of linear matrix inequalities. To achieve finite-time reaching of the predefined sliding surface, an observer-based adaptive controller is designed. Finally, the effectiveness of the proposed methodology is validated through a practical example. [ABSTRACT FROM AUTHOR]

Advances in deep learning have revolutionized cyber‐physical applications, including the development of autonomous vehicles. However, real‐world collisions involving autonomous control of vehicles have raised significant safety concerns regarding the use of deep neural networks (DNNs) in safety‐critical tasks, particularly perception. The inherent unverifiability of DNNs poses a key challenge in ensuring their safe and reliable operation. In this work, we propose perception simplex (PS), a fault‐tolerant application architecture designed for obstacle detection and collision avoidance. We analyse an existing LiDAR‐based classical obstacle detection algorithm to establish strict bounds on its capabilities and limitations. Such analysis and verification have not been possible for deep learning‐based perception systems yet. By employing verifiable obstacle detection algorithms, PS identifies obstacle existence detection faults in the output of unverifiable DNN‐based object detectors. When faults with potential collision risks are detected, appropriate corrective actions are initiated. Through extensive analysis and software‐in‐the‐loop simulations, we demonstrate that PS provides deterministic fault tolerance against obstacle existence detection faults, establishing a robust safety guarantee. [ABSTRACT FROM AUTHOR]

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]

Today, Smart Assistants (SAs) are supported by significantly improved Natural Language Processing (NLP) and Natural Language Understanding (NLU) engines as well as AI-enabled decision support, enabling efficient information communication, easy appliance/device control, and seamless access to entertainment services, among others. In fact, an increasing number of modern households are being equipped with SAs, which promise to enhance user experience in the context of smart environments through verbal interaction. Currently, the market in SAs is dominated by products manufactured by technology giants that provide well designed off-the-shelf solutions. However, their simple setup and ease of use come with trade-offs, as these SAs abide by proprietary and/or closed-source architectures and offer limited functionality. Their enforced vendor lock-in does not provide (power) users with the ability to build custom conversational applications through their SAs. On the other hand, employing an open-source approach for building and deploying an SA (which comes with a significant overhead) necessitates expertise in multiple domains and fluency in the multimodal technologies used to build the envisioned applications. In this context, this paper proposes a methodology for developing and deploying conversational applications on the edge on top of an open-source software and hardware infrastructure via a multilayer architecture that simplifies low-level complexity and reduces learning overhead. The proposed approach facilitates the rapid development of applications by third-party developers, thereby enabling the establishment of a marketplace of customized applications aimed at the smart assisted living domain, among others. The supporting framework supports application developers, device owners, and ecosystem administrators in building, testing, uploading, and deploying applications, remotely controlling devices, and monitoring device performance. A demonstration of this methodology is presented and discussed focusing on health and assisted living applications for the elderly. [ABSTRACT FROM AUTHOR]

The design of cyber-physical systems (CPS) is challenging due to the heterogeneity of software and hardware components that operate in uncertain environments (e.g., fluctuating workloads), hence they are prone to performance issues. Software performance antipatterns could be a key means to tackle this challenge since they recognize design problems that may lead to unacceptable system performance. This manuscript focuses on modeling and analyzing a variegate set of software performance antipatterns with the goal of quantifying their performance impact on CPS. Starting from the specification of eight software performance antipatterns, we build a baseline queuing network performance model that is properly extended to account for the corresponding bad practices. The approach is applied to a CPS consisting of a network of sensors and experimental results show that performance degradation can be traced back to software performance antipatterns. Sensitivity analysis investigates the peculiar characteristics of antipatterns, such as the frequency of checking the status of resources, that provides quantitative information to software designers to help them identify potential performance problems and their root causes. Quantifying the performance impact of antipatterns on CPS paves the way for future work enabling the automated refactoring of systems to remove these bad practices. [ABSTRACT FROM AUTHOR]

Recently, research has been conducted on mixed reality (MR), which provides immersive visualization and interaction experiences, and on mapping human motions directly onto a robot in a mixed reality (MR) space to achieve a high level of immersion. However, even though the robot is mapped onto the MR space, their surrounding environment is often not mapped sufficiently; this makes it difficult to comfortably perform tasks that require precise manipulation of the objects that are difficult to see from the human perspective. Therefore, we propose a system that allows users to operate a robot in real space by mapping the task environment around the robot on the MR space and performing operations within the MR space. [ABSTRACT FROM AUTHOR]

The active distribution network (ADN) can obtain measurement data, estimate system states, and control distributed energy resources (DERs) and flexible loads to ensure voltage stability. However, the ADN is more vulnerable to cyber attacks due to the recent wave of digitization and automation efforts. In this article, false data injection (FDI) attacks are focused on and they are classified into two types, that is, type I attacks on measurement data and type II attacks on control commands. After studying the impact of these two FDI attacks on the ADN, a new threat is revealed called coordinated FDI attack, which can maximize the voltage deviation by coordinating type I and type II FDI attacks. From the attacker's perspective, the scheme of CFDI is proposed and an algorithm is developed to find the optimal attack strategy. The feasibility of CFDI attacks has been validated on a smart distribution testbed. Moreover, simulation results on an ADN benchmark have demonstrated that CFDI attacks could cause remarkable voltage deviation that may deteriorate the stability of the distribution network. Moreover, the impact of CFDI attacks is higher than pure type I or type II attacks. To mitigate the threat, some countermeasures against CFDI attacks are also proposed. [ABSTRACT FROM AUTHOR]

There is a change in the traditional power system structure as a result of the increased incorporation of microgrids (MGs) into the grid. Multi‐area MGs will emerge as a result, and issues related to them will need to be addressed. Load frequency control (LFC) is a challenge in such structures, which are more complicated due to variations in demand and the stochastic characteristics of renewable energy sources. This paper presents a cascade fuzzy active disturbance rejection control technique to deal with the LFC problem. In order to tune different parameters of controllers, a newly developed heuristic algorithm called the Gazelle optimization algorithm (GOA) is also employed. Moreover, due to the fact that multi‐area MGs are regarded as cyber‐physical systems (CPSs), a relatively new concern for LFC problems is their resilience to cyberattacks such as false data injection (FDI) and denial of service (DoS) attacks. Therefore, this research also presents a novel machine learning approach called parallel attack resilience detection system (PARDS) to deal with the LFC problem in the presence of cyberattacks. The efficiency of the proposed strategy is investigated under different scenarios, such as non‐linearities in the power system or server cyberattacks. [ABSTRACT FROM AUTHOR]

Anomaly detection in industrial control and cyber-physical systems has gained much attention over the past years due to the increasing modernisation and exposure of industrial environments. Current dangers to the connected industry include the theft of industrial intellectual property, denial of service, or the compromise of cloud components; all of which might result in a cyber-attack across the operational network. However, most scientific work employs device logs, which necessitate substantial understanding and preprocessing before they can be used in anomaly detection. In this paper, we propose a network intrusion detection system (NIDS) architecture based on a deep autoencoder trained on network flow data, which has the advantage of not requiring prior knowledge of the network topology or its underlying architecture. Experimental results show that the proposed model can detect anomalies, caused by distributed denial of service attacks, providing a high detection rate and low false alarms, outperforming the state-of-the-art and a baseline model in an unsupervised learning environment. Furthermore, the deep autoencoder model can detect abnormal behaviour in legitimate devices after an attack. We also demonstrate the suitability of the proposed NIDS in a real industrial plant from the alimentary sector, analysing the false positive rate and the viability of the data generation, filtering and preprocessing procedure for a near real time scenario. The suggested NIDS architecture is a low-cost solution that uses only fifteen network-based features, requires minimal processing, operates in unsupervised mode, and is straightforward to deploy in real-world scenarios. [ABSTRACT FROM AUTHOR]

Cyber-Physical Systems (CPS) are integral components of modern, interconnected environments, playing a crucial role in various applications such as smart infrastructure and autonomous systems. These systems operate in complex and ever-evolving settings, where resilience is an essential characteristic. Recently, several Machine Learning (ML) techniques have been proposed to design and implement resilience in CPS. However, this approach has often prioritized performance gains and adaptability enhancements, leading to a disregard for the potential dangers linked to centralized data processing, including data breaches and privacy infringements. To harness the full potential of ML in CPS, this paper introduces a distributed intelligence framework that equally prioritizes security, data privacy, and adaptability. The proposed framework is implemented through the integration of Federated Machine Learning techniques, where the CPS architecture is decentralized, allowing data processing to occur locally on individual nodes or devices. This decentralized approach facilitates the aggregation of insights from multiple sources without the need for centralized data processing, thereby minimizing the risks associated with data breaches and privacy violations. We further validate the viability of the proposed framework through its successful implementation in a real-world industrial CPS application, specifically focused on fault prediction within industrial CPS environments. In addition to its privacy and security benefits, our approach also achieved promising results in terms of accuracy and precision. [ABSTRACT FROM AUTHOR]

This article proposes a novel approach for designing a mode‐dependent H∞$\mathcal {H}_\infty$ full‐order dynamic filter for a cyber‐physical system (CPS) that is subject to polytopic uncertainties. The CPS operates on an unreliable network that is susceptible to transmission failures and Denial of Service (DoS) attacks. The attackers have limited energy resources, and the duration of the DoS attack is limited to a maximum number of consecutive time instants. The network is modeled after a proposed non‐homogeneous Markov chain whose transition probability matrix may feature uncertain and unknown probabilities, which are dependent on time‐varying parameters. The design conditions for the filter are obtained using parameter‐dependent linear matrix inequalities. The proposed filter is shown to be effective in reducing the impact of DoS attacks and transmission failures on the CPS. Numerical experiments are presented to illustrate the efficacy of the proposed filter design method, demonstrating its ability to mitigate the effects of uncertainties and attacks on the CPS. [ABSTRACT FROM AUTHOR]

The nuclear energy sector can benefit from mobile robots for remote inspection and handling, reducing human exposure to radiation. Advances in cyber–physical systems have improved robotic platforms in this sector through digital twin (DT) technology. DTs enhance situational awareness for robot operators, crucial for safety in the nuclear energy sector, and their value is anticipated to increase with the growing complexity of cyber–physical systems. The primary motivation of this work is to rapidly develop and evaluate a robot fleet interface that accounts for these benefits in the context of nuclear environments. Here, we introduce a multimodal immersive DT platform for cyber–physical robot fleets based on the ROS‐Unity 3D framework. The system design enables fleet monitoring and management by integrating building information models, mission parameters, robot sensor data, and multimodal user interaction through traditional and virtual reality interfaces. A modified heuristic evaluation approach, which accounts for the positive and negative aspects of the interface, was introduced to accelerate the iterative design process of our DT platform. Robot operators from leading nuclear research institutions (Sellafield Ltd. and the Japan Atomic Energy Agency) performed a simulated robot inspection mission while providing valuable insights into the design elements of the cyber–physical system. The three usability themes that emerged and inspired our design recommendations for future developers include increasing the interface's flexibility, considering each robot's individuality, and adapting the platform to expand sensor visualization capabilities. [ABSTRACT FROM AUTHOR]

The W3C Web of Things (WoT) is a leading technology that facilitates dynamic information management in the Internet of Things (IoT). In most IoT scenarios, devices and their associated information change continuously, generating a large amount of data. Hence, to correctly use the information and the data generated by different devices, a new perspective of managing and ensuring data quality is recommended. Applying Data Science techniques to create the data model can help to manage and ensure data quality by creating a common schema that can be reused in future projects, as well as producing recommendations to facilitate Service Discovery. In addition, due to the dynamic devices that change over time or under specific circumstances, the data model created must be sufficiently abstract to add new instances and to support new requirements that devices should incorporate. The use of models helps to raise the abstraction level, adapting it to the continuous changes of devices by defining instances associated with the data model. This paper proposes two data models: one for Cyber‐Physical Systems (CPS) to define device information fetched by a Discovery Service, and another for applying Deep Learning in natural language problems through a Transformer approach. The latter matches user queries in natural language sentences with WoT devices or services. These data models expand the Thing Description model to help find similar CPSs by giving a confidence level to each CPS based on features such as security and the number of times the device was accessed. The results show how the proposed models support the search process of CPSs in syntactic and natural language searches. Furthermore, the four levels of the FAIR principles are validated for the proposed data models, thus ensuring the data's transparency, reproducibility, and reusability. [ABSTRACT FROM AUTHOR]

This paper studies the finite-time fuzzy adaptive output feedback resilient control problem for nonlinear cyber-physical systems (CPSs) with sensor attacks and actuator faults. Fuzzy logic systems (FLSs) are used to approximate the unknown nonlinear functions, and a fuzzy state observer is constructed to estimate the unmeasured states. By combining the Nussbaum function with the backstepping control design technique, a fuzzy adaptive resilient control scheme is designed to successfully address the effects of sensor attacks and actuator faults. It is proved that the controlled system is semi-global practical finite-time stability (SGPFS), and the tracking error converges to a small neighborhood of the origin in a finite time interval. Finally, the simulation and comparison results further demonstrate the effectiveness of the designed control method. [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]

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]

The word "Industry 4.0" was first introduced in 2011 at the Hannover Fair and created a wave of revolution in a wide variety of industries. Many organizations and companies want to improve manufacturing efficiency and competitiveness by employing some technologies of Industry 4.0. However, the relevance of those technologies to the goal of Industry 4.0 is often unclear, and the concept of Industry 4.0 is vague to many people. The paper clarifies the concept and driving factors of Industry 4.0 by reviewing the history of industrial revolutions. Then, some key technologies of Industry 4.0 and the state of smart factories are introduced. Besides, the steps to implement a smart factory are presented. Finally, this paper gives a case study from semiconductor manufacturing to demonstrate the benefits and application of a smart factory. [ABSTRACT FROM AUTHOR]

Cyber‐Physical Systems (CPS) are at the forefront of the intersection between information technology and physical operations, revolutionizing industries such as communication, automation, control, and intelligent transportation. However, evaluating the reliability of CPS, especially in the presence of multilateral random attacks, poses a complex challenge. This paper introduces an inventive probabilistic modeling approach to enhance the analysis of CPS reliability under the influence of multilateral random attacks. We provide an overview of fundamental CPS concepts and their significant role in modern society, followed by a comprehensive exploration of multilateral random attacks. Our novel probabilistic modeling approach is presented as a means to improve the accuracy and robustness of CPS reliability assessments when faced with multilateral random attacks. Numerical experiments and simulations validate the effectiveness of our innovative approach, emphasizing its potential to fortify the reliability of Cyber‐Physical Systems. [ABSTRACT FROM AUTHOR]

This paper focuses on policy selection and scheduling of sensors and attackers in cyber-physical systems (CPSs) with multiple sensors under denial-of-service (DoS) attacks. DoS attacks have caused enormous disruption to the regular operation of CPSs, and it is necessary to assess this damage. The state estimation of the CPSs plays a vital role in providing real-time information about their operational status and ensuring accurate prediction and assessment of their security. For a multi-sensor CPS, this paper is different from utilizing robust control methods to characterize the state of the system against DoS attacks, but rather positively analyzes the optimal policy selection of the sensors and the attackers through dynamic programming ideology. To optimize the strategies of both sides, game theory is employed as a means to study the dynamic interaction that occurs between the sensors and the attackers. During the policy iterative optimization process, the sensors and attackers dynamically learn and adjust strategies by incorporating reinforcement learning. In order to explore more state information, the restriction on the set of states is relaxed, i.e., the transfer of states is not limited compulsorily. Meanwhile, the complexity of the proposed algorithm is decreased by introducing a penalty in the reward function. Finally, simulation results show that the proposed algorithm can effectively optimize policy selection and scheduling for CPSs with multiple sensors. [ABSTRACT FROM AUTHOR]

This paper investigates the secure optimal tracking control problem for cyber-physical systems, in which the controller-actuator channel is jammed by denial-of-service attacks. Without leveraging model knowledge, learning-based tracking control algorithms are proposed by utilizing the measured input-output data. The impact of malicious attacks on tracking performance is discussed. More specifically, an augmented system consisted of the system model and the reference model is derived. Then the states of the augmented system are rewritten by utilizing the input, output, and reference trajectory, following which both the Bellman equation and algebraic Riccati equation are given. The conditions of existence and uniqueness of the solution to the algebraic Riccati equation are derived. Furthermore, both policy iteration and value iteration learning-based tracking control algorithms are provided for cyber-physical systems against denial-of-service attacks, and the convergence of the algorithms is also proved. Finally, a DC motor system and a numerical example are given to illustrate the effectiveness of the proposed control algorithms. [ABSTRACT FROM AUTHOR]

BACKGROUND: Sports have been a fundamental component of any culture and legacy for centuries. Athletes are widely regarded as a source of national pride, and their physical well-being is deemed to be of paramount significance. The attainment of optimal performance and injury prevention in athletes is contingent upon physical fitness. Technology integration has implemented Cyber-Physical Systems (CPS) to augment the athletic training milieu. OBJECTIVE: The present study introduces an approach for assessing athlete physical fitness in training environments: the Internet of Things (IoT) and CPS-based Physical Fitness Evaluation Method (IoT-CPS-PFEM). METHODS: The IoT-CPS-PFEM employs a range of IoT-connected sensors and devices to observe and assess the physical fitness of athletes. The proposed methodology gathers information on diverse fitness parameters, including heart rate, body temperature, and oxygen saturation. It employs machine learning algorithms to scrutinize and furnish feedback on the athlete's physical fitness status. RESULTS: The simulation findings illustrate the efficacy of the proposed IoT-CPS-PFEM in identifying the physical fitness levels of athletes, with an average precision of 93%. The method under consideration aims to tackle the existing obstacles of conventional physical fitness assessment techniques, including imprecisions, time lags, and manual data-gathering requirements. The approach of IoT-CPS-PFEM provides the benefits of real-time monitoring, precision, and automation, thereby enhancing an athlete's physical fitness and overall performance to a considerable extent. CONCLUSION: The research findings suggest that the implementation of IoT-CPS-PFEM can significantly impact the physical fitness of athletes and enhance the performance of the Indian sports industry in global competitions. [ABSTRACT FROM AUTHOR]

The article delves into the transformative impact of Industry 5.0 principles on production logistics, aiming to enhance manufacturing efficiency and responsiveness by integrating advanced concepts like the milkrun, real-time scheduling, and optimization within smart factories. Addressing evolving customer needs for rapid customization, it seeks to revolutionize traditional approaches to production logistics, establishing a more adaptive and dynamic manufacturing ecosystem. This aligns with the advent of Industry 5.0, which heralds a new era in technological advancements and digitalization, fostering unprecedented transparency in corporate processes. Going beyond the Fourth Industrial Revolution, it seamlessly integrates the corporate value chain and supply network, emphasizing the importance of leveraging integration opportunities between production management and logistics amidst intensifying market competition. The study meticulously examines the role of production logistics within the corporate logistics system, shedding light on the tangible benefits of real-time scheduling and aiming to provide crucial insights for businesses navigating this new industrial revolution. [ABSTRACT FROM AUTHOR]

The growing demand for efficient, resilient, and sustainable electricity infrastructure has led to the emergence of smart grids as cyber‐physical systems of systems. Co‐simulation has proven an effective tool for their analysis and validation by coordinating independent subsystem simulations. However, the reuse and integration of diverse models in co‐simulation poses challenges, requiring compatibility and integration efforts. In response, this paper proposes a model taxonomy with the purpose of facilitating co‐simulation; it comprises three layers: concrete‐instance models, abstract‐instance models, and type models. The taxonomy contributes to the creation of independently developed models that can be seamlessly integrated into a coupled co‐simulation. Furthermore, it reflects the emergence of digital twins in smart grid engineering by the explicit distinction of abstract and concrete instances. The three‐layer taxonomy was derived and validated through a case study on co‐simulation of electric‐vehicle charging infrastructure. The research further analyzes and formalizes three modeling‐and‐simulation challenges framed through the lens of the taxonomy: the integration of models across all three layers, the merging of layers, and the consolidation of instance models to craft joint co‐simulation scenarios. Finally, three concrete recommendations for research and industrial practice are given. Thereby, the study contributes to the efficient and effective model‐based validation of cyber‐physical systems of systems using co‐simulation. [ABSTRACT FROM AUTHOR]

Rising levels of risk as cyber‐attackers look to exploit system vulnerabilities threatens the Air Traffic Control industry. Attacks on Air Navigation Service Providers' communications systems may lead to airspace closure and even cause safety issues. This paper presents a novel Model‐Based Systems Engineering method that enables systems engineers, in collaboration with system security and software engineers, to perform threat‐modeling analysis of cyber‐physical systems early in the system development process and incorporate mitigation strategies into the system design. The proposed model‐based method covers few security concepts, including misuse cases, system assets, threats, risks, vulnerabilities, and security control identification. The study found that the proposed method is suitable for conducting security analysis for complex cyber‐physical systems early in the system development process. [ABSTRACT FROM AUTHOR]

Remotely operated vehicles (ROVs) provide practical solutions for a wide range of activities in a particularly challenging domain, despite their dependence on support ships and operators. Recent advancements in AI, machine learning, predictive analytics, control theories, and sensor technologies offer opportunities to make ROVs (semi) autonomous in their operations and to remotely test and monitor their dynamics. This study moves towards that goal by formulating a complete navigation, guidance, and control (NGC) system for a six DoF BlueROV2, offering a solution to the current challenges in the field of marine robotics, particularly in the areas of power supply, communication, stability, operational autonomy, localization, and trajectory planning. The vehicle can operate (semi) autonomously, relying on a sensor acoustic USBL localization system, tethered communication with the surface vessel for power, and a line of sight (LOS) guidance system. This strategy transforms the path control problem into a heading control problem, aligning the vehicle's movement with a dynamically calculated reference point along the desired path. The control system uses PID controllers implemented in the navigator flight controller board. Additionally, an infrastructure has been developed that synchronizes and communicates between the real ROV and its digital twin within the Unity environment. The digital twin acts as a visual representation of the ROV's movements and considers hydrodynamic behaviors. This approach combines the physical properties of the ROV with the advanced simulation and analysis capabilities of its digital counterpart. All findings were validated at the Point Rouge port located in Marseille and at the port of Ancona. The NGC implemented has proven positive vehicle stability and trajectory tracking in time despite external interferences. Additionally, the digital part has proven to be a reliable infrastructure for a future bidirectional communication system. [ABSTRACT FROM AUTHOR]

The sensor‐enabled in‐vehicle communication and infrastructure‐centric vehicle‐to‐everything (V2X) communications have significantly contributed to the spark in the amount of data exchange in the connected and autonomous vehicles (CAV) environment. The growing vehicular communications pose a potential cyber security risk considering online vehicle hijacking. Therefore, there is a critical need to prioritize the cyber security issues in the CAV research theme. In this context, this paper presents a cyber security analysis of connected vehicle traffic environments (CyACV). Specifically, potential cyber security attacks in CAV are critically investigated and validated via experimental data sets. Trust in V2X communication for connected vehicles is explored in detail focusing on trust computation and trust management approaches and related challenges. A wide range of trust‐based cyber security solutions for CAV have been critically investigated considering their strengths and weaknesses. Open research directions have been highlighted as potential new research themes in CAV cyber security area. [ABSTRACT FROM AUTHOR]

In light of the ever-increasing complexity of cyber–physical systems (CPSs) and information technology networking systems (ITNs), cyber ranges (CRs) have emerged as a promising solution by providing theoretical and practical cybersecurity knowledge for participants' skill improvement toward a safe work environment. This research adds to the extant respective literature, exploring the architectural composition of CRs. It aims to improve the understanding of their design and how they are deployed, expanding skill levels in constructing better CRs. Our research follows the PRISMA methodology guidelines for transparency, which includes a search flow of articles based on specific criteria and quality valuation of selected articles. To extract valuable research datasets, we identify keyword co-occurrences that selected articles are concentrated on. In the context of literature evidence, we identify key attributes and trends, providing details of CRs concerning their architectural composition and underlying infrastructure, along with today's challenges and future research directions. A total of 102 research articles' qualitative analyses reveal a lack of adequate architecture examination when CR elements and services interoperate with other CR elements and services participating, leading to gaps that increase the administration burden. We posit that the results of this study can be leveraged as a baseline for future enhancements toward the development of CRs. [ABSTRACT FROM AUTHOR]

This work describes the collaboration practices of a community of interest in the UK that brings together cybersecurity professionals with a shared interest in improving supply chain cybersecurity for Operational Technology (OT) environments. This research emphasizes the need for collective responsibility between organizations and provides a set of principles for adopting a code of practice and partnership approach to supply chain cybersecurity. This work has enabled cybersecurity experience from several critical infrastructure sectors, including energy, rail, aviation, water, health, and food, to analyze the uptake and practical use of existing supply chain guidance, identifying gaps and challenges. The community has examined touch points with the supply chain and identified improvements related to the communication of cybersecurity requirements, technical and commercial engagement between customers and suppliers, and in the tailoring of implementations towards operational technology contexts. Communicating the context of securing cyber-physical systems is an essential perspective for this community. This work exemplifies a partnership framework and is translating experiences into useful guidance, particularly for OT systems, to improve cybersecurity levels across multiple contributors to critical infrastructure systems. [ABSTRACT FROM AUTHOR]

In today's highly advanced information technology environment, modern network and communication technologies are widely used in monitoring and controlling power systems. These technologies have evolved significantly. They now form a high-performance digital system known as the cyber–physical power system. However, vulnerabilities in communication networks present growing threats to these systems. This paper seeks to enhance the accurate assessment of the security posture of cyber-physical power systems by inferring attackers' intentions. A threat modeling approach based on Bayesian attack graphs is presented, employing Bayesian networks to define and evaluate potential threats that attackers could pose to different system infrastructures. The paper initially conducts a qualitative analysis of the system's threats, constructing a directed graph structure and establishing conditional probability tables among nodes based on prior knowledge. Subsequently, methods are developed to compute the threat levels at different system nodes using real-time detected attack events. Further analysis methods and security assessment metrics are also developed to identify attack paths and quantify system security. Finally, a Bayesian attack graph is constructed in accordance with the system's structure. In practical scenarios, the attack path analysis method can predict the most vulnerable attack paths, while the absolute values of the security assessment metrics indicate the overall risk level of the system. [ABSTRACT FROM AUTHOR]

Transmission lines, the core part of the transmission and distribution system in the smart grid, require effective, efficient, and reliable protective measures against faults to avoid severe damage to physical infrastructure and financial losses. Due to their growing popularity, machine learning models are used in fault detection and classification, whose performances can be severely affected by cyber‐attacks due to their data dependency, posing a critical concern. Hence, this paper introduces false data injection attacks to address the vulnerability of machine learning‐based fault detectors and classifiers. A comparative study of 9 detection models and 6 classification models under normal conditions and during a combination of two models of false data injection attacks is presented to evaluate the severity of cyber‐attacks. Experimental results show that highly accurate models in normal conditions are more susceptible to cyber‐attacks, with up to 69% and 28% degradations in accuracy for fault detectors and classifiers, respectively. Furthermore, the detection models are found to be more vulnerable to cyber‐attacks than the classification models. With no robust detectors and classifiers being found, this work addresses the importance of developing attack‐resilient fault detection and classification schemes considering their academic and industrial significance. [ABSTRACT FROM AUTHOR]

Abstract 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.

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 The active distribution network (ADN) can obtain measurement data, estimate system states, and control distributed energy resources (DERs) and flexible loads to ensure voltage stability. However, the ADN is more vulnerable to cyber attacks due to the recent wave of digitization and automation efforts. In this article, false data injection (FDI) attacks are focused on and they are classified into two types, that is, type I attacks on measurement data and type II attacks on control commands. After studying the impact of these two FDI attacks on the ADN, a new threat is revealed called coordinated FDI attack, which can maximize the voltage deviation by coordinating type I and type II FDI attacks. From the attacker's perspective, the scheme of CFDI is proposed and an algorithm is developed to find the optimal attack strategy. The feasibility of CFDI attacks has been validated on a smart distribution testbed. Moreover, simulation results on an ADN benchmark have demonstrated that CFDI attacks could cause remarkable voltage deviation that may deteriorate the stability of the distribution network. Moreover, the impact of CFDI attacks is higher than pure type I or type II attacks. To mitigate the threat, some countermeasures against CFDI attacks are also proposed.

Abstract There is a change in the traditional power system structure as a result of the increased incorporation of microgrids (MGs) into the grid. Multi‐area MGs will emerge as a result, and issues related to them will need to be addressed. Load frequency control (LFC) is a challenge in such structures, which are more complicated due to variations in demand and the stochastic characteristics of renewable energy sources. This paper presents a cascade fuzzy active disturbance rejection control technique to deal with the LFC problem. In order to tune different parameters of controllers, a newly developed heuristic algorithm called the Gazelle optimization algorithm (GOA) is also employed. Moreover, due to the fact that multi‐area MGs are regarded as cyber‐physical systems (CPSs), a relatively new concern for LFC problems is their resilience to cyberattacks such as false data injection (FDI) and denial of service (DoS) attacks. Therefore, this research also presents a novel machine learning approach called parallel attack resilience detection system (PARDS) to deal with the LFC problem in the presence of cyberattacks. The efficiency of the proposed strategy is investigated under different scenarios, such as non‐linearities in the power system or server cyberattacks.

Abstract The sensor‐enabled in‐vehicle communication and infrastructure‐centric vehicle‐to‐everything (V2X) communications have significantly contributed to the spark in the amount of data exchange in the connected and autonomous vehicles (CAV) environment. The growing vehicular communications pose a potential cyber security risk considering online vehicle hijacking. Therefore, there is a critical need to prioritize the cyber security issues in the CAV research theme. In this context, this paper presents a cyber security analysis of connected vehicle traffic environments (CyACV). Specifically, potential cyber security attacks in CAV are critically investigated and validated via experimental data sets. Trust in V2X communication for connected vehicles is explored in detail focusing on trust computation and trust management approaches and related challenges. A wide range of trust‐based cyber security solutions for CAV have been critically investigated considering their strengths and weaknesses. Open research directions have been highlighted as potential new research themes in CAV cyber security area.

Nowadays, manufacturing firms need the reconfigurability capability to be responsive in the current context characterised by unpredictable and frequent market changes and the reduction of product life cycle. Despite the relevance of the subject, a challenge for practitioners is the development of a strategy aimed to increase the level of reconfigurability with long-term goals of customisation and responsiveness. Moreover, traditional manufacturing paradigms are disrupted by the transformation of manufacturing systems in cyber-physical systems (CPS), thus introducing innovative means also to increase the level of reconfigurability in manufacturing systems. This study investigates how the technologies underlying CPS support the reconfigurability capability along system life cycle. Thus the technologies underlying CPS are classified into seven categories and it is shown how they enable the sequence of utilisation of the reconfigurability characteristics (modularity, integrability, diagnosability, scalability, convertibility and customisation) along the system life cycle. The results of the study can guide practitioners in developing reconfigurability as a strategic capability. Moreover, different directions for future research can be considered, as discussed in the conclusion. [ABSTRACT FROM AUTHOR]