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.

Analysis of security in cyber-physical systems

In recent years,cyber-physical systems（CPSs）have received much attention from both the academic world and the industrial world,which refer to a deep integration and coordination of physical and computational resources.Typical examples of CPSs can be found in smart grids,smart transportation systems,industrial control systems,water supply systems,and so on.Furthermore,many military systems are also CPSs.

Cyber Resilience through Real-Time Threat Analysis in Information Security

This paper examines how cybersecurity is developing and how it relates to more conventional information security. Although information security and cyber security are sometimes used synonymously, this study contends that they are not the same. The concept of cyber security is explored, which goes beyond protecting information resources to include a wider variety of assets, including people [1]. Protecting information assets is the main goal of traditional information security, with consideration to the human element and how people fit into the security process. On the other hand, cyber security adds a new level of complexity, as people might unintentionally contribute to or become targets of cyberattacks. This aspect presents moral questions since it is becoming more widely accepted that society has a duty to protect weaker members of society, including children [1]. The study emphasizes how important cyber security is on a larger scale, with many countries creating plans and laws to counteract cyberattacks. Nevertheless, a lot of these sources frequently neglect to define the differences or the relationship between information security and cyber security [1]. The paper focus on differentiating between cybersecurity and information security on a larger scale. The study also highlights other areas of cybersecurity which includes defending people, social norms, and vital infrastructure from threats that arise from online in addition to information and technology protection. It contends that ethical issues and the human factor are becoming more and more important in protecting assets in the digital age, and that cyber security is a paradigm shift in this regard [1].

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.

Security and Privacy Challenges in Cyber-Physical Systems

Cyber-Physical Systems, or Smart-Embedded Systems, are co-engineered for the integration of physical, computational and networking resources. These resources are used to develop an efficient base for enhancing the quality of services in all areas of life and achieving a classier lifestyle in terms of a required service’s functionality and timing. Cyber-Physical Systems (CPSs) complement the need to have smart products (e.g., homes, hospitals, airports, cities). In other words, regulate the three kinds of resources available: physical, computational, and networking. This regulation supports communication and interaction between the human word and digital word to find the required intelligence in all scopes of life, including Telecommunication, Power Generation and Distribution, and Manufacturing. Data Security is among the most important issues to be considered in recent technologies. Because Cyber-Physical Systems consist of interacting complex components and middle-ware, they face real challenges in being secure against cyber-attacks while functioning efficiently and without affecting or degrading their performance. This study gives a detailed description of CPSs, their challenges (including cyber-security attacks), characteristics, and related technologies. We also focus on the tradeoff between security and performance in CPS, and we present the most common Side Channel Attacks on the implementations of cryptographic algorithms (symmetric: AES and asymmetric: RSA) with the countermeasures against these attacks.

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.

A Review on Cybersecurity Analysis,Attack Detection,and Attack Defense Methods in Cyber-physical Power Systems

Potential malicious cyber-attacks to power systems which are connected to a wide range of stakeholders from the top to tail will impose significant societal risks and challenges.The timely detection and defense are of crucial importance for safe and reliable operation of cyber-physical power systems(CPPSs).This paper presents a comprehensive review of some of the latest attack detection and defense strategies.Firstly,the vulnerabilities brought by some new information and communication technologies(ICTs)are analyzed,and their impacts on the security of CPPSs are discussed.Various malicious cyber-attacks on cyber and physical layers are then analyzed within CPPSs framework,and their features and negative impacts are discussed.Secondly,two current mainstream attack detection methods including state estimation based and machine learning based methods are analyzed,and their benefits and drawbacks are discussed.Moreover,two current mainstream attack defense methods including active defense and passive defense methods are comprehensively discussed.Finally,the trends and challenges in attack detection and defense strategies in CPPSs are provided.

Fine-Tuning Cyber Security Defenses: Evaluating Supervised Machine Learning Classifiers for Windows Malware Detection

Malware attacks on Windows machines pose significant cybersecurity threats,necessitating effective detection and prevention mechanisms.Supervised machine learning classifiers have emerged as promising tools for malware detection.However,there remains a need for comprehensive studies that compare the performance of different classifiers specifically for Windows malware detection.Addressing this gap can provide valuable insights for enhancing cybersecurity strategies.While numerous studies have explored malware detection using machine learning techniques,there is a lack of systematic comparison of supervised classifiers for Windows malware detection.Understanding the relative effectiveness of these classifiers can inform the selection of optimal detection methods and improve overall security measures.This study aims to bridge the research gap by conducting a comparative analysis of supervised machine learning classifiers for detecting malware on Windows systems.The objectives include Investigating the performance of various classifiers,such as Gaussian Naïve Bayes,K Nearest Neighbors(KNN),Stochastic Gradient Descent Classifier(SGDC),and Decision Tree,in detecting Windows malware.Evaluating the accuracy,efficiency,and suitability of each classifier for real-world malware detection scenarios.Identifying the strengths and limitations of different classifiers to provide insights for cybersecurity practitioners and researchers.Offering recommendations for selecting the most effective classifier for Windows malware detection based on empirical evidence.The study employs a structured methodology consisting of several phases:exploratory data analysis,data preprocessing,model training,and evaluation.Exploratory data analysis involves understanding the dataset’s characteristics and identifying preprocessing requirements.Data preprocessing includes cleaning,feature encoding,dimensionality reduction,and optimization to prepare the data for training.Model training utilizes various supervised classifiers,and their performance is evaluated using metrics such as accuracy,precision,recall,and F1 score.The study’s outcomes comprise a comparative analysis of supervised machine learning classifiers for Windows malware detection.Results reveal the effectiveness and efficiency of each classifier in detecting different types of malware.Additionally,insights into their strengths and limitations provide practical guidance for enhancing cybersecurity defenses.Overall,this research contributes to advancing malware detection techniques and bolstering the security posture of Windows systems against evolving cyber threats.

Reliability Analysis of Heterogeneous Sensor-Cloud Systems against Targeted Attacks

Based on the wide application of cloud computing and wireless sensor networks in various fields,the Sensor-Cloud System(SCS)plays an indispensable role between the physical world and the network world.However,due to the close connection and interdependence between the physical resource network and computing resource network,there are security problems such as cascading failures between systems in the SCS.In this paper,we propose a model with two interdependent networks to represent a sensor-cloud system.Besides,based on the percolation theory,we have carried out a formulaic theoretical analysis of the whole process of cascading failure.When the system’s subnetwork presents a steady state where there is no further collapse,we can obtain the largest remaining connected subgroup components and the penetration threshold.Theoretically,this result is the critical maximum that the coupled SCS can withstand.To verify the correctness of the theoretical results,we further carried out actual simulation experiments.The results show that a scale-free network priority attack’s percolation threshold is always less than that of ER network which is priority attacked.Similarly,when the scale-free network is attacked first,adding the power law exponentλcan be more intuitive and more effective to improve the network’s reliability.

Secure state estimation for cyber-physical systems with unknown input sliding mode observer

In recent years, cyber attacks have posed great challenges to the development of cyber-physical systems. It is of great significance to study secure state estimation methods to ensure the safe and stable operation of the system. This paper proposes a secure state estimation for multi-input and multi-output continuous-time linear cyber-physical systems with sparse actuator and sensor attacks. First, for sparse sensor attacks, we propose an adaptive switching mechanism to mitigate the impact of sparse sensor attacks by filtering out their attack modes. Second, an unknown input sliding mode observer is designed to not only observe the system states, sensor attack signals, and measurement noise present in the system but also counteract the effects of sparse actuator attacks through an unknown input matrix. Finally, for the design of an unknown input sliding mode state observer, the feasibility of the observing system is demonstrated by means of Lyapunov functions. Additionally, simulation experiments are conducted to show the effectiveness of this method.

Comparison of Chinese and Foreign Urban Public Security Treatment Mechanisms and Analysis of Policy References

From the viewpoint of disaster prevention in the urban areas, this paper analyzes the value of urban public security system, selects typical cases to make a comparison of Chinese and foreign public security treatment mechanisms, and makes systematic summarization of the Chinese and foreign historic experiences. Then, the paper puts forward some designs on structure of the Chinese urban public security system structure. This paper is of the opinion that at present the management of urban public security concerning unexpected incidents should be strengthened. The effective way is to set up the sound urban public security system, urban public security mechanism and related laws and regulations.

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.

Analysis and design of secure cyber-physical systems

Cyber-physical systems （CPSs） are new emerging systems that seamlessly integrate physical systems, communication systems and computation systems. Their wide use has been witnessed in the past decades in many crossdiscipline fields such as smart energy systems, industrial process control, aerospace and automobile engineering, health-care and assisted living, to just name a few. For many of these systems, secure operations are of key con- cerns. In particular, for some safety-critical applications, security is of paramount importance. Diverse motivations and strong incentives exist everywhere and at any time for launching malicious attacks on the CPSs, for example, economic reasons （e.g., by reducing or even not paying electricity charge） and terrorism the purpose of which is apparent.

A survey on the security of cyber-physical systems

Cyber-physical systems （CPSs） are integrations of computation, communication, control and physical processes. Typical examples where CPSs are deployed include smart grids, civil infrastructure, medical devices and manufacturing. Security is one of the most important issues that should be investigated in CPSs and hence has received much attention in recent years. This paper surveys recent results in this area and mainly focusses on three important categories： attack detection, attack design and secure estimation and control. We also discuss several future research directions including risk assessment, modeling of attacks and attacks design, counter-attack strategy and testbed and validation.

On modeling of electrical cyber-physical systems considering cyber security

This paper establishes a new framework for modeling electrical cyber-physical systems(ECPSs),integrating both power grids and communication networks. To model the communication network associated with a power transmission grid, we use a mesh network that considers the features of power transmission grids such as high-voltage levels, long-transmission distances, and equal importance of each node. Moreover, bidirectional links including data uploading channels and command downloading channels are assumed to connect every node in the communication network and a corresponding physical node in the transmission grid. Based on this model,the fragility of an ECPS is analyzed under various cyber attacks including denial-of-service(Do S) attacks, replay attacks, and false data injection attacks. Control strategies such as load shedding and relay protection are also verified using this model against these attacks.

Security for cyber-physical systems: Secure control against known-plaintext attack

There has been a surge of interests in the security of cyber-physical systems(CPSs), yet it is commonly assumed that the adversary has a full knowledge of physical system models. This paper argues that such an unrealistic assumption can be relaxed: the adversary might still be able to identify the system model by passively observing the control input and sensory data. In such a setup, the attack with knowledge of input-output data can be categorized as a Known-Plaintext Attack. A necessary and sufficient condition has been provided, under which the adversary can uniquely obtain the knowledge of the underlying physical system.From the defender's perspective, a secure controller design—which exhibits a low rank structure—is proposed which renders the system unidentifiable to the adversary, while trading off the control system's performance. Finally, a numerical example has been provided to demonstrate the effectiveness of the proposed secure controller design.

Big Data Analysis in Smart Manufacturing: A Review

The technological evolution emerges a unified (Industrial) Internet of Things network, where loosely coupled smart manufacturing devices build smart manufacturing systems and enable comprehensive collaboration possibilities that increase the dynamic and volatility of their ecosystems. On the one hand, this evolution generates a huge field for exploitation, but on the other hand also increases complexity including new challenges and requirements demanding for new approaches in several issues. One challenge is the analysis of such systems that generate huge amounts of (continuously generated) data, potentially containing valuable information useful for several use cases, such as knowledge generation, key performance indicator (KPI) optimization, diagnosis, predication, feedback to design or decision support. This work presents a review of Big Data analysis in smart manufacturing systems. It includes the status quo in research, innovation and development, next challenges, and a comprehensive list of potential use cases and exploitation possibilities.

A Cyber-Attack Detection System Using Late Fusion Aggregation Enabled Cyber-Net

Today,securing devices connected to the internet is challenging as security threats are generated through various sources.The protection of cyber-physical systems from external attacks is a primary task.The presented method is planned on the prime motive of detecting cybersecurity attacks and their impacted parameters.The proposed architecture employs the LYSIS dataset and formulates Multi Variant Exploratory Data Analysis(MEDA)through Principle Component Analysis(PCA)and Singular Value Decompo-sition(SVD)for the extraction of unique parameters.The feature mappings are analyzed with Recurrent 2 Convolutional Neural Network(R2CNN)and Gradient Boost Regression(GBR)to identify the maximum correlation.Novel Late Fusion Aggregation enabled with Cyber-Net(LFAEC)is the robust derived algorithm.The quantitative analysis uses predicted threat points with actual threat variables from which mean and difference vectors areevaluated.The performance of the presented system is assessed against the parameters such as Accuracy,Precision,Recall,and F1 Score.The proposed method outperformed by 98% to 100% in all quality measures compared to existing methods.

基于系统理论的中国-东盟网络安全合作系统构建研究

[研究目的]针对中国-东盟网络安全合作在观念、制度、人才、技术等方面存在的问题,构建合作系统框架,提出合作路径及解决方案,旨在提高中国-东盟网络安全合作的水平。[研究方法]通过文献分析,结合系统理论,从系统的要素、结构(功能)、环境等维度进行研究。[研究结论]中国与东盟在构建网络安全合作系统时可以实施以下路径:在系统要素方面,以网络空间命运共同体理念展开合作、以建立统一的网络安全制度规范框架进行合作、以构建网络安全人才培养培训教育体系保障合作、以创新网络安全技术加强合作;在系统结构(功能)方面,增强系统不同要素间的协同联动、准确把握系统要素最优与结构(功能)最优关系;在系统环境方面,在梳理可供利用的外部环境的基础上,重点利用经济、政治、文化环境优化系统。

基于CLPN的系统安全性分析方法

随着安全性攸关系统的智能化、自动化发展,系统规模和复杂度急剧增加,传统基于链式/树式安全性因果模型的安全性分析方法在非线性致危因素、非失效致危因素分析方面存在很大的局限性,现代基于系统理论的安全性因果模型虽然在航空航天、核电能源等多个领域得到有效性验证,但目前该类方法尚缺乏严格统一的分析步骤和自动化分析工具。为此,提出复杂系统控制过程的建模工具——控制逻辑Petri网(CLPN),对控制过程中的活动及其之间的交互影响关系进行形式化描述,并对系统CLPN模型的可达图进行失效扩展,在不影响安全性分析的前提下尽量避免因失效事件建模造成的分析模型规模激增。基于CLPN模型,以系统理论事故模型与过程(STAMP)系统安全性因果模型危险因素分类为标准,在可达性分析的基础上对作为系统致危因素的危险控制活动进行探索,实现系统安全性的自动化分析。最后,通过实例分析和方法对比,对所提方法的可用性和有效性进行验证。实验结果表明,基于CLPN的系统安全性分析方法在结果完备性和分析效率方面具有较大的优势。