Analysis and Modeling of Mobile Phone Activity Data Using Interactive Cyber-Physical Social System

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 hasmillions of subscribers and thus generatesmassive 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.

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.

Novel cyber-physical collaborative detection and localization method against dynamic load altering attacks in smart energy grids

Owing to the integration of energy digitization and artificial intelligence technology,smart energy grids can realize the stable,efficient and clean operation of power systems.However,the emergence of cyber-physical attacks,such as dynamic load-altering attacks(DLAAs)has introduced great challenges to the security of smart energy grids.Thus,this study developed a novel cyber-physical collaborative security framework for DLAAs in smart energy grids.The proposed framework integrates attack prediction in the cyber layer with the detection and localization of attacks in the physical layer.First,a data-driven method was proposed to predict the DLAA sequence in the cyber layer.By designing a double radial basis function network,the influence of disturbances on attack prediction can be eliminated.Based on the prediction results,an unknown input observer-based detection and localization method was further developed for the physical layer.In addition,an adaptive threshold was designed to replace the traditional precomputed threshold and improve the detection performance of the DLAAs.Consequently,through the collaborative work of the cyber-physics layer,injected DLAAs were effectively detected and located.Compared with existing methodologies,the simulation results on IEEE 14-bus and 118-bus power systems verified the superiority of the proposed cyber-physical collaborative detection and localization against DLAAs.

Risk-Balanced Routing Strategy for Service Function Chains of Cyber-Physical Power System Considering Cross-Space Cascading Failure

Cyber-physical power system(CPPS)has significantly improved the operational efficiency of power systems.However,cross-space cascading failures may occur due to the coupling characteristics,which poses a great threat to the safety and reliability of CPPS,and there is an acute need to reduce the probability of these failures.Towards this end,this paper first proposes a cascading failure index to identify and quantify the importance of different information in the same class of communication services.On this basis,a joint improved risk-balanced service function chain routing strategy(SFC-RS)is proposed,which is modeled as a robust optimization problem and solved by column-and-constraint generation(C-CG)algorithm.Compared with the traditional shortest-path routing algorithm,the superiority of SFC-RS is verified in the IEEE 30-bus system.The results demonstrate that SFC-RS effectively mitigates the risk associated with information transmission in the network,enhances information transmission accessibility,and effectively limits communication disruption from becoming the cause of cross-space cascading failures.

Steps Toward Industry 5.0:Building“6S”Parallel Industries With Cyber-Physical-Social Intelligence

Very recently,intensive discussions and studies on Industry 5.0 have sprung up and caused the attention of researchers,entrepreneurs,and policymakers from various sectors around the world.However,there is no consensus on why and what is Industry 5.0 yet.In this paper,we define Industry 5.0from its philosophical and historical origin and evolution,emphasize its new thinking on virtual-real duality and human-machine interaction,and introduce its new theory and technology based on parallel intelligence(PI),artificial societies,computational experiments,and parallel execution(the ACP method),and cyber-physical-social systems(CPSS).Case studies and applications of Industry 5.0 over the last decade have been briefly summarized and analyzed with suggestions for its future development.We believe that Industry 5.0 of virtual-real interactive parallel industries has great potentials and is critical for building smart societies.Steps are outlined to ensure a roadmap that would lead to a smooth transition from CPS-based Industry 4.0 to CPSS-based Industry 5.0 for a better world which is Safe in physical spaces,S ecure in cyberspaces,Sustainable in ecology,Sensitive in individual privacy and rights,Service for all,and Smartness of all.

网络攻击下非线性CPSs的事件触发自适应控制

本文研究了网络攻击下一类控制方向未知的非线性信息物理系统(Cyber-Physical Systems,CPSs)的自适应输出反馈控制问题。通过引入合适的线性状态变换,将原系统转化为便于输出反馈控制设计的新系统。采用Nussbaum增益函数方法克服未知的控制系数和网络攻击所带来的设计困难。基于观测器状态和受攻击的测量输出信号,采用神经网络逼近方法,提出了一种新的事件触发自适应输出反馈控制策略。该控制策略不但大大降低数据的传输量,而且保证闭环系统是半全局一致最终有界的。另外,采用基于非线性滤波器的动态面控制方法可以克服反步法设计中惯有的“复杂性爆炸”问题。最后,通过机械手系统验证所提出控制方法的有效性和实用性。

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%.

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.

基于CPSS的综合能源微网群参与调频辅助服务成本建模分析

随着双碳战略的实施,新能源在电力系统中所占比例不断提高,系统频率调节能力逐渐下降.针对该问题,对分布式综合能源微网群参与电力系统调频辅助服务展开深入研究.首先,基于微网个体聚合后参与调频市场的理念,阐述综合能源微网参与调频辅助服务市场的模式;其次,建立综合能源微网群的信息-物理-社会模型,基于该模型计算微网群体的调频成本,成本模型中考虑多种调频方式,并利用灰色定权聚类方法对微网的社会属性进行分类,用以仿真分析微网个体参与调频市场的意愿及概率值;然后,基于蒙特卡罗模拟提出微网聚合商参与调频辅助服务时的成本计算方法,为聚合商参与调频市场报价提供基础.最后,对一个拥有5个综合能源微网的聚合商进行仿真分析,得到其调频成本的变化曲线.仿真结果显示微网个体的社会属性对聚合商的调频成本影响可达到6.921%,在报价过程中需要充分计及微网社会属性所带来的不确定性风险.

多源数据环境下产业竞争情报智慧服务平台构建研究--以“三元世界”和CPSS理论为基础

面对多源数据环境和复杂信息场景,产业竞争情报服务优化升级成为助力新一轮产业发展和产业链优化的抓手。我国产业亟须构建体系化的竞争情报智慧服务平台,赋能大变局时代和复杂国际竞争中的产业健康发展。本文聚焦于多源数据环境,采用半结构化访谈方法进行产业竞争情报服务平台现状调研,以“三元世界”和CPSS(cyber-physical-socialsystems)系统建模思想为依据,实现物理世界、信息空间和人类社会中的多源数据多维立体解析和数据融合,借助解构思想进行产业竞争情报元素分解和重组,构建产业竞争情报智慧服务平台模型,并提出“情报协同采集与处理-多源数据跨模态、跨结构融合-联盟链分布式情报数据存储-情报知识挖掘与发现-定制化智慧服务”的产业竞争情报智慧服务平台实施路径。

CPSS视角下城市建筑与配电网高韧性协调运行:研究述评与展望

“韧性”是城市应对自然灾害、提高风险防控能力的重要属性。频发的大停电事故会造成严重的经济和民生损失,阻碍城市发展甚至威胁国家能源安全“,韧性配电网”是“韧性城市”的必然要求与基本保障。高比例建筑负荷是城市配电网的主要特征,恢复供电后的建筑负荷行为异于常态,不仅对电力供应提出了更高的要求,而且给予配电网韧性以提升潜力。城市建筑作为用户与电力系统、电力市场、大数据等深度交互的主要场所之一,其与配电网的协调运行是一个复杂的信息-物理-社会系统(CPSS)。首先,文中重点关注城市配电网负荷恢复阶段,总结了当前城市建筑与配电网高韧性协调运行在物理、社会、信息维度面临的问题;然后,分别对极端条件下城市建筑与配电网的耦合机理和建模(物理维度)、城市建筑产消者行为刻画与激励机制设计(社会维度)、配电网灾后态势感知与数据共享(信息维度)三方面的研究现状进行了述评;最后,总结了CPSS视角下城市建筑与配电网高韧性耦合运行研究所面临的四重挑战,并对未来的研究路径进行了展望。

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.

Cyber-Physical-Social System Between a Humanoid Robot and a Virtual Human Through a Shared Platform for Adaptive Agent Ecology

Two artificial agents(a humanoid robot and a virtual human) are enriched with various similar intelligence,autonomy, functionalities and interaction modalities. The agents are integrated in the form of a cyber-physical-social system(CPSS) through a shared communication platform to create a social ecology. In the ecology, the agents collaborate(assist each other) to perform a real-world task(search for a hidden object)for the benefits of humans. A robot-virtual human bilateral trust model is derived and a real-time trust measurement method is developed. The role of taking initiative in the collaboration is switched between the agents following a finite state machine model triggered by bilateral trust, which results in a mixedinitiative collaboration. A scheme is developed to evaluate the performance of the agents in the ecology through the CPSS.The results show that the robot and the virtual human perform satisfactorily in the collaboration through the CPSS. The results thus prove the effectiveness of the real-world ecology between artificial agents of heterogeneous realities through a shared platform based on trust-triggered mixed-initiatives. The results can help develop adaptive social ecology comprising intelligent agents of heterogeneous realities to assist humans in various tasks through collaboration between the agents in the form of a CPSS.

平行智能与CPSS:三十年发展的回顾与展望

社会物理信息系统(Cyber-physical-social systems,CPSS)在传统物理信息系统(Cyber-physical systems,CPS)的基础上纳入对社会信号及社会关系的考虑,利用网络世界近乎无限的人力、数据和信息资源,突破物理世界有限的资源约束以及时空的限制.然而,CPSS中人类和社会行为的复杂性加剧了实际系统和其模型之间的建模鸿沟,使得系统的形态演变为“默顿系统”.对此,以ACP方法为核心的平行智能(Parallel intelligence,PI)框架通过组合人工系统(Artificial systems,A)、计算实验(Computational experiments,C)、平行执行(Parallel execution,P)三个过程,为跨越这一鸿沟提供了可行的路径.具体而言,ACP将模型从系统解析器转变为数据生成器,使原本难以控制的“默顿系统”可测试、可计算、可验证,为复杂系统中“涌现”和“收敛”的对立统一确立了方法基础.本文从平行控制与智能控制、平行机器人与平行制造、平行管理与智能交通、平行医学与智慧健康、平行生态与平行社会、平行经济系统与社会计算、平行军事系统以及平行认知与平行哲学这八个方面阐述面向CPSS的平行智能应用成果.最后,对CPSS未来的发展方向和技术趋势进行了讨论与展望.

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.

Learning-Based Switched Reliable Control of Cyber-Physical Systems With Intermittent Communication Faults

This study deals with reliable control problems in data-driven cyber-physical systems(CPSs) with intermittent communication faults, where the faults may be caused by bad or broken communication devices and/or cyber attackers. To solve them, a watermark-based anomaly detector is proposed, where the faults are divided to be either detectable or undetectable.Secondly, the fault's intermittent characteristic is described by the average dwell-time(ADT)-like concept, and then the reliable control issues, under the undetectable faults to the detector, are converted into stabilization issues of switched systems. Furthermore,based on the identifier-critic-structure learning algorithm, a datadriven switched controller with a prescribed-performance-based switching law is proposed, and by the ADT approach, a tolerated fault set is given. Additionally, it is shown that the presented switching laws can improve the system performance degradation in asynchronous intervals, where the degradation is caused by the fault-maker-triggered switching rule, which is unknown for CPS operators. Finally, an illustrative example validates the proposed method.

Cyber-Physical Production Systems for Data-Driven,Decentralized,and Secure Manufacturing-A Perspective

With the concepts of Industry 4.0 and smart manufacturing gaining popularity,there is a growing notion that conventional manufacturing will witness a transition toward a new paradigm,targeting innovation,automation,better response to customer needs,and intelligent systems.Within this context,this review focuses on the concept of cyber–physical production system(CPPS)and presents a holistic perspective on the role of the CPPS in three key and essential drivers of this transformation:data-driven manufacturing,decentralized manufacturing,and integrated blockchains for data security.The paper aims to connect these three aspects of smart manufacturing and proposes that through the application of data-driven modeling,CPPS will aid in transforming manufacturing to become more intuitive and automated.In turn,automated manufacturing will pave the way for the decentralization of manufacturing.Layering blockchain technologies on top of CPPS will ensure the reliability and security of data sharing and integration across decentralized systems.Each of these claims is supported by relevant case studies recently published in the literature and from the industry;a brief on existing challenges and the way forward is also provided.