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.

Machine Learning Security Defense Algorithms Based on Metadata Correlation Features

With the popularization of the Internet and the development of technology,cyber threats are increasing day by day.Threats such as malware,hacking,and data breaches have had a serious impact on cybersecurity.The network security environment in the era of big data presents the characteristics of large amounts of data,high diversity,and high real-time requirements.Traditional security defense methods and tools have been unable to cope with the complex and changing network security threats.This paper proposes a machine-learning security defense algorithm based on metadata association features.Emphasize control over unauthorized users through privacy,integrity,and availability.The user model is established and the mapping between the user model and the metadata of the data source is generated.By analyzing the user model and its corresponding mapping relationship,the query of the user model can be decomposed into the query of various heterogeneous data sources,and the integration of heterogeneous data sources based on the metadata association characteristics can be realized.Define and classify customer information,automatically identify and perceive sensitive data,build a behavior audit and analysis platform,analyze user behavior trajectories,and complete the construction of a machine learning customer information security defense system.The experimental results show that when the data volume is 5×103 bit,the data storage integrity of the proposed method is 92%.The data accuracy is 98%,and the success rate of data intrusion is only 2.6%.It can be concluded that the data storage method in this paper is safe,the data accuracy is always at a high level,and the data disaster recovery performance is good.This method can effectively resist data intrusion and has high air traffic control security.It can not only detect all viruses in user data storage,but also realize integrated virus processing,and further optimize the security defense effect of user big data.

Fortifying Healthcare Data Security in the Cloud:A Comprehensive Examination of the EPM-KEA Encryption Protocol

A new era of data access and management has begun with the use of cloud computing in the healthcare industry.Despite the efficiency and scalability that the cloud provides, the security of private patient data is still a majorconcern. Encryption, network security, and adherence to data protection laws are key to ensuring the confidentialityand integrity of healthcare data in the cloud. The computational overhead of encryption technologies could leadto delays in data access and processing rates. To address these challenges, we introduced the Enhanced ParallelMulti-Key Encryption Algorithm (EPM-KEA), aiming to bolster healthcare data security and facilitate the securestorage of critical patient records in the cloud. The data was gathered from two categories Authorization forHospital Admission (AIH) and Authorization for High Complexity Operations.We use Z-score normalization forpreprocessing. The primary goal of implementing encryption techniques is to secure and store massive amountsof data on the cloud. It is feasible that cloud storage alternatives for protecting healthcare data will become morewidely available if security issues can be successfully fixed. As a result of our analysis using specific parametersincluding Execution time (42%), Encryption time (45%), Decryption time (40%), Security level (97%), and Energyconsumption (53%), the system demonstrated favorable performance when compared to the traditional method.This suggests that by addressing these security concerns, there is the potential for broader accessibility to cloudstorage solutions for safeguarding healthcare data.

Novel Static Security and Stability Control of Power Systems Based on Artificial Emotional Lazy Q-Learning

The stability problem of power grids has become increasingly serious in recent years as the size of novel power systems increases.In order to improve and ensure the stable operation of the novel power system,this study proposes an artificial emotional lazy Q-learning method,which combines artificial emotion,lazy learning,and reinforcement learning for static security and stability analysis of power systems.Moreover,this study compares the analysis results of the proposed method with those of the small disturbance method for a stand-alone power system and verifies that the proposed lazy Q-learning method is able to effectively screen useful data for learning,and improve the static security stability of the new type of power system more effectively than the traditional proportional-integral-differential control and Q-learning methods.

Information Security in the Cloud: Emerging Trends and Challenges

This article explores the evolution of cloud computing, its advantages over traditional on-premises infrastructure, and its impact on information security. The study presents a comprehensive literature review covering various cloud infrastructure offerings and security models. Additionally, it deeply analyzes real-life case studies illustrating successful cloud migrations and highlights common information security threats in current cloud computing. The article concludes by offering recommendations to businesses to protect themselves from cloud data breaches and providing insights into selecting a suitable cloud services provider from an information security perspective.

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].

Machine Learning Empowered Security and Privacy Architecture for IoT Networks with the Integration of Blockchain

The Internet of Things(IoT)is growing rapidly and impacting almost every aspect of our lives,fromwearables and healthcare to security,traffic management,and fleet management systems.This has generated massive volumes of data and security,and data privacy risks are increasing with the advancement of technology and network connections.Traditional access control solutions are inadequate for establishing access control in IoT systems to provide data protection owing to their vulnerability to single-point OF failure.Additionally,conventional privacy preservation methods have high latency costs and overhead for resource-constrained devices.Previous machine learning approaches were also unable to detect denial-of-service(DoS)attacks.This study introduced a novel decentralized and secure framework for blockchain integration.To avoid single-point OF failure,an accredited access control scheme is incorporated,combining blockchain with local peers to record each transaction and verify the signature to access.Blockchain-based attribute-based cryptography is implemented to protect data storage privacy by generating threshold parameters,managing keys,and revoking users on the blockchain.An innovative contract-based DOS attack mitigation method is also incorporated to effectively validate devices with intelligent contracts as trusted or untrusted,preventing the server from becoming overwhelmed.The proposed framework effectively controls access,safeguards data privacy,and reduces the risk of cyberattacks.The results depict that the suggested framework outperforms the results in terms of accuracy,precision,sensitivity,recall,and F-measure at 96.9%,98.43%,98.8%,98.43%,and 98.4%,respectively.

Sensitive Information Security Based on Elliptic Curves

The elliptic curve cryptography algorithm represents a major advancement in the field of computer security. This innovative algorithm uses elliptic curves to encrypt and secure data, providing an exceptional level of security while optimizing the efficiency of computer resources. This study focuses on how elliptic curves cryptography helps to protect sensitive data. Text is encrypted using the elliptic curve technique because it provides great security with a smaller key on devices with limited resources, such as mobile phones. The elliptic curves cryptography of this study is better than using a 256-bit RSA key. To achieve equivalent protection by using the elliptic curves cryptography, several Python libraries such as cryptography, pycryptodome, pyQt5, secp256k1, etc. were used. These technologies are used to develop a software based on elliptic curves. If built, the software helps to encrypt and decrypt data such as a text messages and it offers the authentication for the communication.

A Review of Cybersecurity Challenges in Small Business: The Imperative for a Future Governance Framework

Technological shifts—coupled with infrastructure, techniques, and applications for big data—have created many new opportunities, business models, and industry expansion that benefit entrepreneurs. At the same time, however, entrepreneurs are often unprepared for cybersecurity needs—and the policymakers, industry, and nonprofit groups that support them also face technological and knowledge constraints in keeping up with their needs. To improve the ability of entrepreneurship research to understand, identify, and ultimately help address cybersecurity challenges, we conduct a literature review on the state of cybersecurity. The research highlights the necessity for additional investigation to aid small businesses in securing their confidential data and client information from cyber threats, thereby preventing the potential shutdown of the business.

Systematic Literature Review on Cloud Computing Security: Threats and Mitigation Strategies

Cloud computing plays a significant role in modern information technology, providing organizations with numerous benefits, including flexibility, scalability, and cost-efficiency. However, it has become essential for organizations to ensure the security of their applications, data, and cloud-based networks to use cloud services effectively. This systematic literature review aims to determine the latest information regarding cloud computing security, with a specific emphasis on threats and mitigation strategies. Additionally, it highlights some common threats related to cloud computing security, such as distributed denial-of-service (DDoS) attacks, account hijacking, malware attacks, and data breaches. This research also explores some mitigation strategies, including security awareness training, vulnerability management, security information and event management (SIEM), identity and access management (IAM), and encryption techniques. It discusses emerging trends in cloud security, such as integrating artificial intelligence (AI) and machine learning (ML), serverless computing, and containerization, as well as the effectiveness of the shared responsibility model and its related challenges. The importance of user awareness and the impact of emerging technologies on cloud security have also been discussed in detail to mitigate security risks. A literature review of previous research and scholarly articles has also been conducted to provide insights regarding cloud computing security. It shows the need for continuous research and innovation to address emerging threats and maintain a security-conscious culture in the company.

Enhancing Cybersecurity through AI and ML: Strategies, Challenges, and Future Directions

The landscape of cybersecurity is rapidly evolving due to the advancement and integration of Artificial Intelligence (AI) and Machine Learning (ML). This paper explores the crucial role of AI and ML in enhancing cybersecurity defenses against increasingly sophisticated cyber threats, while also highlighting the new vulnerabilities introduced by these technologies. Through a comprehensive analysis that includes historical trends, technological evaluations, and predictive modeling, the dual-edged nature of AI and ML in cybersecurity is examined. Significant challenges such as data privacy, continuous training of AI models, manipulation risks, and ethical concerns are addressed. The paper emphasizes a balanced approach that leverages technological innovation alongside rigorous ethical standards and robust cybersecurity practices. This approach facilitates collaboration among various stakeholders to develop guidelines that ensure responsible and effective use of AI in cybersecurity, aiming to enhance system integrity and privacy without compromising security.

Status and prospect of security management of marine data storage

China's marine data includes marine hydrology,marine meteorology,marine biology,marine chemistry,marine substrate,marine geophysical,seabed topography and other categories of data,the total amount of data reaches the magnitude of PB,and the amount of data is still increasing.The safe management of these marine data storage is the basis of building a Smart Ocean.This paper discusses the current situation of security management of marine data storage in China,analyzes the problems of security management in domestic marine data storage,and puts forward suggestions.

Optimizing Network Security via Ensemble Learning: A Nexus with Intrusion Detection

Network intrusion detection systems need to be updated due to the rise in cyber threats. In order to improve detection accuracy, this research presents a strong strategy that makes use of a stacked ensemble method, which combines the advantages of several machine learning models. The ensemble is made up of various base models, such as Decision Trees, K-Nearest Neighbors (KNN), Multi-Layer Perceptrons (MLP), and Naive Bayes, each of which offers a distinct perspective on the properties of the data. The research adheres to a methodical workflow that begins with thorough data preprocessing to guarantee the accuracy and applicability of the data. In order to extract useful attributes from network traffic data—which are essential for efficient model training—feature engineering is used. The ensemble approach combines these models by training a Logistic Regression model meta-learner on base model predictions. In addition to increasing prediction accuracy, this tiered approach helps get around the drawbacks that come with using individual models. High accuracy, precision, and recall are shown in the model’s evaluation of a network intrusion dataset, indicating the model’s efficacy in identifying malicious activity. Cross-validation is used to make sure the models are reliable and well-generalized to new, untested data. In addition to advancing cybersecurity, the research establishes a foundation for the implementation of flexible and scalable intrusion detection systems. This hybrid, stacked ensemble model has a lot of potential for improving cyberattack prevention, lowering the likelihood of cyberattacks, and offering a scalable solution that can be adjusted to meet new threats and technological advancements.

新型电力系统数据跨域流通泛安全边界防护技术

新型电力系统建设涉及多业务系统、多部门、多方主体间进行海量、异构数据的交互和共享,电力数据的内外部网络环境与安全形势日趋复杂化,数据流通的脆弱性风险加剧。首先,分析新型电力系统下数据流的类型与特性,概括电力数据流通安全防护面临的新形势;其次,基于专用数据处理器(DPU)的高性能流量编排和多功能安全网关能力,构建面向电力数据跨域流通安全增强的泛安全边界,凭借数据面可编程技术沟通网络安全与数据安全双维度安全能力,提出基于DPU的数据跨域流通协同防护技术应用方案;最后,阐释DPU在不同电力通信网络层次的部署方式、价值与关键技术,分析现阶段DPU在电力领域应用存在的挑战。

Proof of Activity Protocol for IoMT Data Security

The Internet of Medical Things(IoMT)is an online device that senses and transmits medical data from users to physicians within a time interval.In,recent years,IoMT has rapidly grown in the medicalfield to provide healthcare services without physical appearance.With the use of sensors,IoMT applications are used in healthcare management.In such applications,one of the most important factors is data security,given that its transmission over the network may cause obtrusion.For data security in IoMT systems,blockchain is used due to its numerous blocks for secure data storage.In this study,Blockchain-assisted secure data management framework(BSDMF)and Proof of Activity(PoA)protocol using malicious code detection algorithm is used in the proposed data security for the healthcare system.The main aim is to enhance the data security over the networks.The PoA protocol enhances high security of data from the literature review.By replacing the malicious node from the block,the PoA can provide high security for medical data in the blockchain.Comparison with existing systems shows that the proposed simulation with BSD-Malicious code detection algorithm achieves higher accuracy ratio,precision ratio,security,and efficiency and less response time for Blockchain-enabled healthcare systems.

A Robust Data Hiding Reversible Technique for Improving the Security in e-Health Care System

The authenticity and integrity of healthcare is the primary objective.Numerous reversible watermarking schemes have been developed to improve the primary objective but increasing the quantity of embedding data leads to covering image distortion and visual quality resulting in data security detection.A trade-off between robustness,imperceptibility,and embedded capacity is difficult to achieve with current algorithms due to limitations in their ability.Keeping this purpose insight,an improved reversibility watermarking methodology is proposed to maximize data embedding capacity and imperceptibility while maintaining data security as a primary concern.A key is generated by a random path with minimum bit flipping is selected in the 4 × 4 block to gain access to the data embedding patterns.The random path's complex structure ensures data security.Data of various sizes(8 KB,16 KB,32 KB)are used to analyze image imperceptibility and evaluate quality factors.The proposed reversible watermarking methodology performance is tested under standard structures PSNR,SSIM,and MSE.The results revealed that the MRI watermarked images are imperceptible,like the cover image when LSB is 3 bits plane.Our proposed reversible watermarking methodology outperforms other related techniques in terms of average PSNR(49.29).Experiment results show that the suggested reversible watermarking method improves data embedding capacity and imperceptibility compared to existing state-of-the-art approaches.

Evaluating Security of Big Data Through Fuzzy Based Decision-Making Technique

In recent years,it has been observed that the disclosure of information increases the risk of terrorism.Without restricting the accessibility of information,providing security is difficult.So,there is a demand for time tofill the gap between security and accessibility of information.In fact,security tools should be usable for improving the security as well as the accessibility of information.Though security and accessibility are not directly influenced,some of their factors are indirectly influenced by each other.Attributes play an important role in bridging the gap between security and accessibility.In this paper,we identify the key attributes of accessibility and security that impact directly and indirectly on each other,such as confidentiality,integrity,availability,and severity.The significance of every attribute on the basis of obtained weight is important for its effect on security during the big data security life cycle process.To calculate the proposed work,researchers utilised the Fuzzy Analytic Hierarchy Process(Fuzzy AHP).Thefindings show that the Fuzzy AHP is a very accurate mechanism for determining the best security solution in a real-time healthcare context.The study also looks at the rapidly evolving security technologies in healthcare that could help improve healthcare services and the future prospects in this area.

Secured Cyber Security Algorithm for Healthcare System Using Blockchain Technology

Blockchain technology is critical in cyber security.The most recent cryptographic strategies may be hacked as efforts are made to build massive elec-tronic circuits.Because of the ethical and legal implications of a patient’s medical data,cyber security is a critical and challenging problem in healthcare.The image secrecy is highly vulnerable to various types of attacks.As a result,designing a cyber security model for healthcare applications necessitates extra caution in terms of data protection.To resolve this issue,this paper proposes a Lionized Golden Eagle based Homomorphic Elapid Security(LGE-HES)algorithm for the cybersecurity of blockchain in healthcare networks.The blockchain algorithm preserves the security of the medical image by performing hash function.The execution of this research is carried out by MATLAB software.The suggested fra-mework was tested utilizing Computed Tumor(CT)pictures and MRI image data-sets,and the simulation results revealed the proposed model’s profound implications.During the simulation,94.9%of malicious communications were recognized and identified effectively,according to the total outcomes statistics.The suggested model’s performance is also compared to that of standard approaches in terms of Root Mean Square Error(RMSE),Peak Signal to Noise Ratio(PSNR),Mean Square Error(MSE),time complexity,and other factors.

Towards Developing Privacy-Preserved Data Security Approach(PP-DSA)in Cloud Computing Environment

In the present scenario of rapid growth in cloud computing models,several companies and users started to share their data on cloud servers.However,when the model is not completely trusted,the data owners face several security-related problems,such as user privacy breaches,data disclosure,data corruption,and so on,during the process of data outsourcing.For addressing and handling the security-related issues on Cloud,several models were proposed.With that concern,this paper develops a Privacy-Preserved Data Security Approach(PP-DSA)to provide the data security and data integrity for the out-sourcing data in Cloud Environment.Privacy preservation is ensured in this work with the Efficient Authentication Technique(EAT)using the Group Signature method that is applied with Third-Party Auditor(TPA).The role of the auditor is to secure the data and guarantee shared data integrity.Additionally,the Cloud Service Provider(CSP)and Data User(DU)can also be the attackers that are to be handled with the EAT.Here,the major objective of the work is to enhance cloud security and thereby,increase Quality of Service(QoS).The results are evaluated based on the model effectiveness,security,and reliability and show that the proposed model provides better results than existing works.

数据要素流通与安全的研究范畴与未来发展趋势

针对数据从信息技术时代的受控共享向数据技术时代的数据要素泛在流通演化的趋势,分析了数据共享与数据要素流通的差异,阐述了什么样的数据才能成为数据要素,提出了数据成为数据要素所必须具有的6个属性,定义了数据要素流通模型及主要环节,明确了数据要素流通的研究范畴,梳理了数据要素流通研究范畴的相关概念,厘清了研究范畴所涵盖的关键核心技术,并对未来需要突破的关键技术进行了展望。