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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]

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]

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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]

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]

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 automotive industry is experiencing a significant shift, transitioning from traditional hardware-centric systems to more advanced software-defined architectures. This change is enabling enhanced autonomy, connectivity, safety, and improved in-vehicle experiences. Service-oriented architecture is crucial for achieving software-defined vehicles and creating new business opportunities for original equipment manufacturers. A software update approach that is rich in variability and based on a Merkle tree approach is proposed for new vehicle architecture requirements. Given the complexity of software updates in vehicles, particularly when dealing with multiple distributed electronic control units, this software-centric approach can be optimized to handle various architectures and configurations, ensuring consistency across all platforms. In this paper, our software update approach is expanded to cover the solution space of the feature-based product line engineering, and we show how to combine our approach with product line engineering in creative and unique ways to form a software-defined vehicle modular architecture. Then, we offer insights into the design of the Merkle trees utilized in our approach, emphasizing the relationship among the software modules, with a focus on their impact on software update performance. This approach streamlines the software update process and ensures that the safety as well as the security of the vehicle are continuously maintained. [ABSTRACT FROM AUTHOR]

This study addresses the critical issue of cardiovascular diseases (CVDs) as the leading cause of death globally, emphasizing the importance of stent delivery catheter manufacturing. Traditional manufacturing processes, reliant on destructive end-of-batch sampling, present significant financial and quality challenges. This research addresses this challenge by proposing a novel approach: a closed-loop cyber-physical production system (CPPS) employing non-destructive process analytical technology (PAT). Through a mixed-method approach combining a comprehensive literature review and the development of a CPPS prototype, the study demonstrates the potential for real-time quality control, reduced production costs, and increased manufacturing efficiency. Initial findings showcase the system's effectiveness in streamlining production, enhancing stability, and minimizing defects, translating to substantial financial savings and improved product quality. This work extends the author's previous research by comparing the validated system's performance to that of pre-implementation manual workflows and inspections, highlighting tangible and intangible improvements brought by the new system. This paves the way for advanced control strategies to revolutionize medical device manufacturing. Furthermore, the study proposes a generalized CPPS framework applicable across diverse regulated environments, ensuring optimal processing conditions and adherence to stringent regulatory standards. The research concludes with the successful demonstration of innovative approaches and technologies, leading to improved product quality, patient safety, and operational efficiency in the medical device industry. [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]

Mobile networks possess significant information and thus are considered a gold mine for the researcher's community. The call detail records (CDR) of a mobile network are used to identify the network's efficacy and the mobile user's behavior. It is evident from the recent literature that cyber-physical systems (CPS) were used in the analytics and modeling of telecom data. In addition, CPS is used to provide valuable services in smart cities. In general, a typical telecom company has millions of subscribers and thus generates massive amounts of data. From this aspect, data storage, analysis, and processing are the key concerns. To solve these issues, herein we propose a multilevel cyber-physical social system (CPSS) for the analysis and modeling of large internet data. Our proposed multilevel system has three levels and each level has a specific functionality. Initially, raw Call Detail Data (CDR) was collected at the first level. Herein, the data preprocessing, cleaning, and error removal operations were performed. In the second level, data processing, cleaning, reduction, integration, processing, and storage were performed. Herein, suggested internet activity record measures were applied. Our proposed system initially constructs a graph and then performs network analysis. Thus proposed CPSS system accurately identifies different areas of internet peak usage in a city (Milan city). Our research is helpful for the network operators to plan effective network configuration, management, and optimization of resources. [ABSTRACT FROM AUTHOR]

The advent of Industry 5.0 marks a transformative era where Cyber-Physical Systems (CPSs) seamlessly integrate physical processes with advanced digital technologies. However, as industries become increasingly interconnected and reliant on smart digital technologies, the intersection of physical and cyber domains introduces novel security considerations, endangering the entire industrial ecosystem. The transition towards a more cooperative setting, including humans and machines in Industry 5.0, together with the growing intricacy and interconnection of CPSs, presents distinct and diverse security and privacy challenges. In this regard, this study provides a comprehensive review of security and privacy concerns pertaining to CPSs in the context of Industry 5.0. The review commences by providing an outline of the role of CPSs in Industry 5.0 and then proceeds to conduct a thorough review of the different security risks associated with CPSs in the context of Industry 5.0. Afterward, the study also presents the privacy implications inherent in these systems, particularly in light of the massive data collection and processing required. In addition, the paper delineates potential avenues for future research and provides countermeasures to surmount these challenges. Overall, the study underscores the imperative of adopting comprehensive security and privacy strategies within the context of Industry 5.0. [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 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]

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]

Abstract 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 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 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.

The automotive industry is experiencing a significant shift, transitioning from traditional hardware-centric systems to more advanced software-defined architectures. This change is enabling enhanced autonomy, connectivity, safety, and improved in-vehicle experiences. Service-oriented architecture is crucial for achieving software-defined vehicles and creating new business opportunities for original equipment manufacturers. A software update approach that is rich in variability and based on a Merkle tree approach is proposed for new vehicle architecture requirements. Given the complexity of software updates in vehicles, particularly when dealing with multiple distributed electronic control units, this software-centric approach can be optimized to handle various architectures and configurations, ensuring consistency across all platforms. In this paper, our software update approach is expanded to cover the solution space of the feature-based product line engineering, and we show how to combine our approach with product line engineering in creative and unique ways to form a software-defined vehicle modular architecture. Then, we offer insights into the design of the Merkle trees utilized in our approach, emphasizing the relationship among the software modules, with a focus on their impact on software update performance. This approach streamlines the software update process and ensures that the safety as well as the security of the vehicle are continuously maintained.

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.

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]

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]

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]

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]