Security Analysis in Smart Agriculture: Insights from a Cyber-Physical System Application

Smart agriculture modifies traditional farming practices,and offers innovative approaches to boost production and sustainability by leveraging contemporary technologies.In today’s world where technology is everything,these technologies are utilized to streamline regular tasks and procedures in agriculture,one of the largest and most significant industries in every nation.This research paper stands out from existing literature on smart agriculture security by providing a comprehensive analysis and examination of security issues within smart agriculture systems.Divided into three main sections-security analysis,system architecture and design and risk assessment of Cyber-Physical Systems(CPS)applications-the study delves into various elements crucial for smart farming,such as data sources,infrastructure components,communication protocols,and the roles of different stakeholders such as farmers,agricultural scientists and researchers,technology providers,government agencies,consumers and many others.In contrast to earlier research,this work analyzes the resilience of smart agriculture systems using approaches such as threat modeling,penetration testing,and vulnerability assessments.Important discoveries highlight the concerns connected to unsecured communication protocols,possible threats from malevolent actors,and vulnerabilities in IoT devices.Furthermore,the study suggests enhancements for CPS applications,such as strong access controls,intrusion detection systems,and encryption protocols.In addition,risk assessment techniques are applied to prioritize mitigation tactics and detect potential hazards,addressing issues like data breaches,system outages,and automated farming process sabotage.The research sets itself apart even more by presenting a prototype CPS application that makes use of a digital temperature sensor.This application was first created using a Tinkercad simulator and then using actual hardware with Arduino boards.The CPS application’s defenses against potential threats and vulnerabilities are strengthened by this integrated approach,which distinguishes this research for its depth and usefulness in the field of smart agriculture security.

A Review on Security and Privacy Issues Pertaining to Cyber-Physical Systems in the Industry 5.0 Era

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.

A Deep CNN-LSTM-Based Feature Extraction for Cyber-Physical System Monitoring

A potential concept that could be effective for multiple applications is a“cyber-physical system”(CPS).The Internet of Things(IoT)has evolved as a research area,presenting new challenges in obtaining valuable data through environmental monitoring.The existing work solely focuses on classifying the audio system of CPS without utilizing feature extraction.This study employs a deep learning method,CNN-LSTM,and two-way feature extraction to classify audio systems within CPS.The primary objective of this system,which is built upon a convolutional neural network(CNN)with Long Short Term Memory(LSTM),is to analyze the vocalization patterns of two different species of anurans.It has been demonstrated that CNNs,when combined with mel-spectrograms for sound analysis,are suitable for classifying ambient noises.Initially,the data is augmented and preprocessed.Next,the mel spectrogram features are extracted through two-way feature extraction.First,Principal Component Analysis(PCA)is utilized for dimensionality reduction,followed by Transfer learning for audio feature extraction.Finally,the classification is performed using the CNN-LSTM process.This methodology can potentially be employed for categorizing various biological acoustic objects and analyzing biodiversity indexes in natural environments,resulting in high classification accuracy.The study highlights that this CNNLSTM approach enables cost-effective and resource-efficient monitoring of large natural regions.The dissemination of updated CNN-LSTM models across distant IoT nodes is facilitated flexibly and dynamically through the utilization of CPS.

Distributed dynamic event-based finite-time dissipative synchronization control for semi-Markov switched fuzzy cyber-physical systems against random packet losses

This paper is concerned with the finite-time dissipative synchronization control problem of semi-Markov switched cyber-physical systems in the presence of packet losses, which is constructed by the Takagi–Sugeno fuzzy model. To save the network communication burden, a distributed dynamic event-triggered mechanism is developed to restrain the information update. Besides, random packet dropouts following the Bernoulli distribution are assumed to occur in sensor to controller channels, where the triggered control input is analyzed via an equivalent method containing a new stochastic variable. By establishing the mode-dependent Lyapunov–Krasovskii functional with augmented terms, the finite-time boundness of the error system limited to strict dissipativity is studied. As a result of the help of an extended reciprocally convex matrix inequality technique, less conservative criteria in terms of linear matrix inequalities are deduced to calculate the desired control gains. Finally, two examples in regard to practical systems are provided to display the effectiveness of the proposed theory.

Stochastic Models to Mitigate Sparse Sensor Attacks in Continuous-Time Non-Linear Cyber-Physical Systems

Cyber-Physical Systems are very vulnerable to sparse sensor attacks.But current protection mechanisms employ linear and deterministic models which cannot detect attacks precisely.Therefore,in this paper,we propose a new non-linear generalized model to describe Cyber-Physical Systems.This model includes unknown multivariable discrete and continuous-time functions and different multiplicative noises to represent the evolution of physical processes and randomeffects in the physical and computationalworlds.Besides,the digitalization stage in hardware devices is represented too.Attackers and most critical sparse sensor attacks are described through a stochastic process.The reconstruction and protectionmechanisms are based on aweighted stochasticmodel.Error probability in data samples is estimated through different indicators commonly employed in non-linear dynamics(such as the Fourier transform,first-return maps,or the probability density function).A decision algorithm calculates the final reconstructed value considering the previous error probability.An experimental validation based on simulation tools and real deployments is also carried out.Both,the new technology performance and scalability are studied.Results prove that the proposed solution protects Cyber-Physical Systems against up to 92%of attacks and perturbations,with a computational delay below 2.5 s.The proposed model shows a linear complexity,as recursive or iterative structures are not employed,just algebraic and probabilistic functions.In conclusion,the new model and reconstructionmechanism can protect successfully Cyber-Physical Systems against sparse sensor attacks,even in dense or pervasive deployments and scenarios.

Aquila Optimization with Machine Learning-Based Anomaly Detection Technique in Cyber-Physical Systems

Cyber-physical system(CPS)is a concept that integrates every computer-driven system interacting closely with its physical environment.Internet-of-things(IoT)is a union of devices and technologies that provide universal interconnection mechanisms between the physical and digital worlds.Since the complexity level of the CPS increases,an adversary attack becomes possible in several ways.Assuring security is a vital aspect of the CPS environment.Due to the massive surge in the data size,the design of anomaly detection techniques becomes a challenging issue,and domain-specific knowledge can be applied to resolve it.This article develops an Aquila Optimizer with Parameter Tuned Machine Learning Based Anomaly Detection(AOPTML-AD)technique in the CPS environment.The presented AOPTML-AD model intends to recognize and detect abnormal behaviour in the CPS environment.The presented AOPTML-AD framework initially pre-processes the network data by converting them into a compatible format.Besides,the improved Aquila optimization algorithm-based feature selection(IAOA-FS)algorithm is designed to choose an optimal feature subset.Along with that,the chimp optimization algorithm(ChOA)with an adaptive neuro-fuzzy inference system(ANFIS)model can be employed to recognise anomalies in the CPS environment.The ChOA is applied for optimal adjusting of the membership function(MF)indulged in the ANFIS method.The performance validation of the AOPTML-AD algorithm is carried out using the benchmark dataset.The extensive comparative study reported the better performance of the AOPTML-AD technique compared to recent models,with an accuracy of 99.37%.

Deep Learning Based Attack Detection for Cyber-Physical System Cybersecurity:A Survey

With the booming of cyber attacks and cyber criminals against cyber-physical systems(CPSs),detecting these attacks remains challenging.It might be the worst of times,but it might be the best of times because of opportunities brought by machine learning(ML),in particular deep learning(DL).In general,DL delivers superior performance to ML because of its layered setting and its effective algorithm for extract useful information from training data.DL models are adopted quickly to cyber attacks against CPS systems.In this survey,a holistic view of recently proposed DL solutions is provided to cyber attack detection in the CPS context.A six-step DL driven methodology is provided to summarize and analyze the surveyed literature for applying DL methods to detect cyber attacks against CPS systems.The methodology includes CPS scenario analysis,cyber attack identification,ML problem formulation,DL model customization,data acquisition for training,and performance evaluation.The reviewed works indicate great potential to detect cyber attacks against CPS through DL modules.Moreover,excellent performance is achieved partly because of several highquality datasets that are readily available for public use.Furthermore,challenges,opportunities,and research trends are pointed out for future research.

Blockchain-Assisted Secure Fine-Grained Searchable Encryption for a Cloud-Based Healthcare Cyber-Physical System

The concept of sharing of personal health data over cloud storage in a healthcare-cyber physical system has become popular in recent times as it improves access quality.The privacy of health data can only be preserved by keeping it in an encrypted form,but it affects usability and flexibility in terms of effective search.Attribute-based searchable encryption(ABSE)has proven its worth by providing fine-grained searching capabilities in the shared cloud storage.However,it is not practical to apply this scheme to the devices with limited resources and storage capacity because a typical ABSE involves serious computations.In a healthcare cloud-based cyber-physical system(CCPS),the data is often collected by resource-constraint devices;therefore,here also,we cannot directly apply ABSE schemes.In the proposed work,the inherent computational cost of the ABSE scheme is managed by executing the computationally intensive tasks of a typical ABSE scheme on the blockchain network.Thus,it makes the proposed scheme suitable for online storage and retrieval of personal health data in a typical CCPS.With the assistance of blockchain technology,the proposed scheme offers two main benefits.First,it is free from a trusted authority,which makes it genuinely decentralized and free from a single point of failure.Second,it is computationally efficient because the computational load is now distributed among the consensus nodes in the blockchain network.Specifically,the task of initializing the system,which is considered the most computationally intensive,and the task of partial search token generation,which is considered as the most frequent operation,is now the responsibility of the consensus nodes.This eliminates the need of the trusted authority and reduces the burden of data users,respectively.Further,in comparison to existing decentralized fine-grained searchable encryption schemes,the proposed scheme has achieved a significant reduction in storage and computational cost for the secret key associated with users.It has been verified both theoretically and practically in the performance analysis section.

Challenges and Requirements for the Application of Industry 4.0:A Special Insight with the Usage of Cyber-Physical System

Considered as a top priority of industrial devel- opment, Industry 4.0 （or Industrie 4.0 as the German ver- sion） has being highlighted as the pursuit of both academy and practice in companies. In this paper, based on the review of state of art and also the state of practice in dif- ferent countries, shortcomings have been revealed as the lacking of applicable framework for the implementation of Industrie 4.0. Therefore, in order to shed some light on the knowledge of the details, a reference architecture is developed, where four perspectives namely manufacturing process, devices, software and engineering have been highlighted. Moreover, with a view on the importance of Cyber-Physical systems, the structure of Cyber-Physical System are established for the in-depth analysis. Further cases with the usage of Cyber-Physical System are also arranged, which attempts to provide some implications to match the theoretical findings together with the experience of companies. In general, results of this paper could be useful for the extending on the theoretical understanding of Industrie 4.0. Additionally, applied framework and proto- types based on the usage of Cyber-Physical Systems are also potential to help companies to design the layout of sensor nets, to achieve coordination and controlling of smart machines, to realize synchronous production with systematic structure, and to extend the usage of information and communication technologies to the maintenance scheduling.

Parallel Reinforcement Learning-Based Energy Efficiency Improvement for a Cyber-Physical System

As a complex and critical cyber-physical system(CPS),the hybrid electric powertrain is significant to mitigate air pollution and improve fuel economy.Energy management strategy(EMS)is playing a key role to improve the energy efficiency of this CPS.This paper presents a novel bidirectional long shortterm memory(LSTM)network based parallel reinforcement learning(PRL)approach to construct EMS for a hybrid tracked vehicle(HTV).This method contains two levels.The high-level establishes a parallel system first,which includes a real powertrain system and an artificial system.Then,the synthesized data from this parallel system is trained by a bidirectional LSTM network.The lower-level determines the optimal EMS using the trained action state function in the model-free reinforcement learning(RL)framework.PRL is a fully data-driven and learning-enabled approach that does not depend on any prediction and predefined rules.Finally,real vehicle testing is implemented and relevant experiment data is collected and calibrated.Experimental results validate that the proposed EMS can achieve considerable energy efficiency improvement by comparing with the conventional RL approach and deep RL.

Variance-Constrained Filtering Fusion for Nonlinear Cyber-Physical Systems With the Denial-of-Service Attacks and Stochastic Communication Protocol

In this paper,a new filtering fusion problem is studied for nonlinear cyber-physical systems under errorvariance constraints and denial-of-service attacks.To prevent data collision and reduce communication cost,the stochastic communication protocol is adopted in the sensor-to-filter channels to regulate the transmission order of sensors.Each sensor is allowed to enter the network according to the transmission priority decided by a set of independent and identicallydistributed random variables.From the defenders’view,the occurrence of the denial-of-service attack is governed by the randomly Bernoulli-distributed sequence.At the local filtering stage,a set of variance-constrained local filters are designed where the upper bounds(on the filtering error covariances)are first acquired and later minimized by appropriately designing filter parameters.At the fusion stage,all local estimates and error covariances are combined to develop a variance-constrained fusion estimator under the federated fusion rule.Furthermore,the performance of the fusion estimator is examined by studying the boundedness of the fused error covariance.A simulation example is finally presented to demonstrate the effectiveness of the proposed fusion estimator.

Analysis of cascading failures of power cyber-physical systems considering false data injection attacks

This study considers the performance impacts of false data injection attacks on the cascading failures of a power cyber-physical system,and identifies vulnerable nodes.First,considering the monitoring and control functions of a cyber network and power flow characteristics of a power network,a power cyber-physical system model is established.Then,the influences of a false data attack on the decision-making and control processes of the cyber network communication processes are studied,and a cascading failure analysis process is proposed for the cyber-attack environment.In addition,a vulnerability evaluation index is defined from two perspectives,i.e.,the topology integrity and power network operation characteristics.Moreover,the effectiveness of a power flow betweenness assessment for vulnerable nodes in the cyberphysical environment is verified based on comparing the node power flow betweenness and vulnerability assessment index.Finally,an IEEE14-bus power network is selected for constructing a power cyber-physical system.Simulations show that both the uplink communication channel and downlink communication channel suffer from false data attacks,which affect the ability of the cyber network to suppress the propagation of cascading failures,and expand the scale of the cascading failures.The vulnerability evaluation index is calculated for each node,so as to verify the effectiveness of identifying vulnerable nodes based on the power flow betweenness.

A Sensorless State Estimation for A Safety-Oriented Cyber-Physical System in Urban Driving:Deep Learning Approach

In today's modern electric vehicles,enhancing the safety-critical cyber-physical system(CPS)'s performance is necessary for the safe maneuverability of the vehicle.As a typical CPS,the braking system is crucial for the vehicle design and safe control.However,precise state estimation of the brake pressure is desired to perform safe driving with a high degree of autonomy.In this paper,a sensorless state estimation technique of the vehicle's brake pressure is developed using a deep-learning approach.A deep neural network(DNN)is structured and trained using deep-learning training techniques,such as,dropout and rectified units.These techniques are utilized to obtain more accurate model for brake pressure state estimation applications.The proposed model is trained using real experimental training data which were collected via conducting real vehicle testing.The vehicle was attached to a chassis dynamometer while the brake pressure data were collected under random driving cycles.Based on these experimental data,the DNN is trained and the performance of the proposed state estimation approach is validated accordingly.The results demonstrate high-accuracy brake pressure state estimation with RMSE of 0.048 MPa.

Passivity-Based Robust Control Against Quantified False Data Injection Attacks in Cyber-Physical Systems

Secure control against cyber attacks becomes increasingly significant in cyber-physical systems(CPSs).False data injection attacks are a class of cyber attacks that aim to compromise CPS functions by injecting false data such as sensor measurements and control signals.For quantified false data injection attacks,this paper establishes an effective defense framework from the energy conversion perspective.Then,we design an energy controller to dynamically adjust the system energy changes caused by unknown attacks.The designed energy controller stabilizes the attacked CPSs and ensures the dynamic performance of the system by adjusting the amount of damping injection.Moreover,with the disturbance attenuation technique,the burden of control system design is simplified because there is no need to design an attack observer.In addition,this secure control method is simple to implement because it avoids complicated mathematical operations.The effectiveness of our control method is demonstrated through an industrial CPS that controls a permanent magnet synchronous motor.

A Security Enhancement Model Based on Switching Edge Strategy in Interdependent Heterogeneous Cyber-Physical Systems

With the advent of cross-domain interconnection,large-scale sensor network systems such as smart grids,smart homes,and intelligent transportation have emerged.These complex network systems often have a CPS(Cyber-Physical System)architecture and are usually composed of multiple interdependent systems.Minimal faults between interdependent networks may cause serious cascading failures between the entire system.Therefore,in this paper,we will explore the robustness detection schemes for interdependent networks.Firstly,by calculating the largest giant connected component in the entire system,the security of interdependent network systems under different attack models is analyzed.Secondly,a comparative analysis of the cascade failure mechanism between interdependent networks under the edge enhancement strategy is carried out.Finally,the simulation results verify the impact of system reliability under different handover edge strategies and show how to choose a better handover strategy to enhance its robustness.The further research work in this paper can also help design how to reduce the interdependence between systems,thereby further optimizing the interdependent network system’s structure to provide practical support for reducing the cascading failures.In the later work,we hope to explore our proposed strategies in the network model of real-world or close to real networks.

Guest Editorial for Special Issue on Cyber-Physical Systems

Ⅰ.Introduction CYBER-PHYSICAL system is a system of collaborating computational elements to control physical entities.The coordination and the tight link between computational,virtual and physical resources in cyber-physical system will have a pervasive effect on our everyday life.The development of cyber-physical system will create new opportunities for the introduction of services that will enhance the quality of life

Towards Aspect Based Components Integration Framework for Cyber-Physical System

Cyber-Physical Systems(CPS)comprise interactive computation,networking,and physical processes.The integrative environment of CPS enables the smart systems to be aware of the surrounding physical world.Smart systems,such as smart health care systems,smart homes,smart transportation,and smart cities,are made up of complex and dynamic CPS.The components integration development approach should be based on the divide and conquer theory.This way multiple interactive components can reduce the development complexity inCPS.As reusability enhances efficiency and consistency in CPS,encapsulation of component functionalities and a well-designed user interface is vital for the better end-user’s Quality of Experience(QoE).Thus,incorrect interaction of interfaces in the cyber-physical system causes system failures.Usually,interface failures occur due to false,and ambiguous requirements analysis and specification.Therefore,to resolve this issue semantic analysis is required for different stakeholders’viewpoint analysis during requirement specification and components analysis.This work proposes a framework to improve the CPS component integration process,starting from requirement specification to prioritization of components for configurable.For semantic analysis and assessing the reusability of specifications,the framework uses text mining and case-based reasoning techniques.The framework has been tested experimentally,and the results show a significant reduction in ambiguity,redundancy,and irrelevancy,as well as increasing accuracy of interface interactions,component selection,and higher user satisfaction.

A Triple Human-Digital Twin Architecture for Cyber-Physical Systems

With the development of information and communication technology and the advent of the Internet of Things(IoT)era,cyber-physical system(CPS)is becoming the trend of products or systems.The deep integration and real-time interaction between the physical world and the virtual world expand system functions.Although there are some CPS implementation guidelines,the virtual world is still relatively abstract compared to the concrete physical world that can be touched through the IoT.Besides that,human is a non-negligible CPS endogenous interactive intelligent component.In this paper,we propose a triple human-digital twin architecture,where the physical objects and the digital twins that are the projections of the physical entities establish the cornerstone of human functioning together.And the hierarchically distributed digital twins grow dynamically with the physical entities along the lifecycle.Furthermore,the interaction and collaboration among the physical objects,the digital twins,and the humans in their respective worlds(the expected world,the interpreted world,and the physical world)integrate the full value chain of the products in anticipation of seamless synergy.Finally,we present a power management digital companion platform for the lunar probe to demonstrate the efficacy of the architecture.

Optimal Machine Learning Enabled Intrusion Detection in Cyber-Physical System Environment

Cyber-attacks on cyber-physical systems(CPSs)resulted to sensing and actuation misbehavior,severe damage to physical object,and safety risk.Machine learning(ML)models have been presented to hinder cyberattacks on the CPS environment;however,the non-existence of labelled data from new attacks makes their detection quite interesting.Intrusion Detection System(IDS)is a commonly utilized to detect and classify the existence of intrusions in the CPS environment,which acts as an important part in secure CPS environment.Latest developments in deep learning(DL)and explainable artificial intelligence(XAI)stimulate new IDSs to manage cyberattacks with minimum complexity and high sophistication.In this aspect,this paper presents an XAI based IDS using feature selection with Dirichlet Variational Autoencoder(XAIIDS-FSDVAE)model for CPS.The proposed model encompasses the design of coyote optimization algorithm(COA)based feature selection(FS)model is derived to select an optimal subset of features.Next,an intelligent Dirichlet Variational Autoencoder(DVAE)technique is employed for the anomaly detection process in the CPS environment.Finally,the parameter optimization of the DVAE takes place using a manta ray foraging optimization(MRFO)model to tune the parameter of the DVAE.In order to determine the enhanced intrusion detection efficiency of the XAIIDS-FSDVAE technique,a wide range of simulations take place using the benchmark datasets.The experimental results reported the better performance of the XAIIDSFSDVAE technique over the recent methods in terms of several evaluation parameters.

Topology-Based and Event-Oriented Approach to Simulation of Cyber-Physical Systems

A simulation model for cyber-physical systems(CPSs)was presented.The model was developed by the method of combination of topology-based and event-oriented that could be used to simulate systems with routing flexibility,service-selection flexibility and service- mode flexibility overall by integrating the strategies related.The validity of the model has been verified by two extensive experiments.