Climate change,natural disasters,pollution,and fast urbanization have made environmental security a more serious international issue.Timely,accurate,and multi-dimensional information is essential in the effective moni...Climate change,natural disasters,pollution,and fast urbanization have made environmental security a more serious international issue.Timely,accurate,and multi-dimensional information is essential in the effective monitoring and management of such complex challenges in the environment.The Earth Observation(EO)systems,including optical sensors,radar sensors,Light Detection and Ranging(LiDAR)sensors,thermal sensors,Unmanned Aerial Vehicle(UAV)sensors,and in-situ sensors,offer a good coverage of space and time,as well as provide useful information on land,water,and atmospheric processes.But the shortcomings or weaknesses of individual sensors,such as their vulnerability to weather conditions,spectral or spatial resolution,and gaps in time,can tend to limit their ability to provide a complete picture of the environment.One of the solutions has been multi-sensor fusion,which combines heterogeneous data and makes it more accurate,robust,and interpretable.This systematic review analyzes the latest methods of multi-sensor fusion,which are machine learning,deep learning,probabilistic models,and hybrid approaches,in terms of methodological principles,preprocessing needs,and computational frameworks.Applications in environmental security are highlighted,which include monitoring natural disasters,monitoring of climate and ecosystem,pollution monitoring,monitoring of land use change,and early warning systems.The review also covers evaluation measures,validation plans,and uncertainty measures,where a strict measure of evaluation is vital to making actionable decisions.Lastly,emerging issues,e.g.,data heterogeneity,computational needs,sensor interoperability,and prospects in the future,e.g.,AI-based adaptive fusion,UAVs and Internet of Things(IoT)integration,and scalable cloud-based systems,are discussed.The synthesis has highlighted the transformational capability of multi-sensor EO in terms of improving the environment in the context of environmental security and sustainable management.展开更多
The accelerated advancement of the Internet of Things(IoT)has generated substantial data,including sensitive and private information.Consequently,it is imperative to guarantee the security of data sharing.While facili...The accelerated advancement of the Internet of Things(IoT)has generated substantial data,including sensitive and private information.Consequently,it is imperative to guarantee the security of data sharing.While facilitating fine-grained access control,Ciphertext Policy Attribute-Based Encryption(CP-ABE)can effectively ensure the confidentiality of shared data.Nevertheless,the conventional centralized CP-ABE scheme is plagued by the issues of keymisuse,key escrow,and large computation,which will result in security risks.This paper suggests a lightweight IoT data security sharing scheme that integrates blockchain technology and CP-ABE to address the abovementioned issues.The integrity and traceability of shared data are guaranteed by the use of blockchain technology to store and verify access transactions.The encryption and decryption operations of the CP-ABE algorithm have been implemented using elliptic curve scalarmultiplication to accommodate lightweight IoT devices,as opposed to themore arithmetic bilinear pairing found in the traditional CP-ABE algorithm.Additionally,a portion of the computation is delegated to the edge nodes to alleviate the computational burden on users.A distributed key management method is proposed to address the issues of key escrow andmisuse.Thismethod employs the edge blockchain to facilitate the storage and distribution of attribute private keys.Meanwhile,data security sharing is enhanced by combining off-chain and on-chain ciphertext storage.The security and performance analysis indicates that the proposed scheme is more efficient and secure.展开更多
1.Introduction Data inference(DInf)is a data security threat in which critical information is inferred from low-sensitivity data.Once regarded as an advanced professional threat limited to intelligence analysts,DInf h...1.Introduction Data inference(DInf)is a data security threat in which critical information is inferred from low-sensitivity data.Once regarded as an advanced professional threat limited to intelligence analysts,DInf has become a widespread risk in the artificial intelligence(AI)era.展开更多
With the integration of informatization and intelligence into the Communication-Based Train Control(CBTC)systems,the system is facing an increasing number of information security threats.As an important method of char...With the integration of informatization and intelligence into the Communication-Based Train Control(CBTC)systems,the system is facing an increasing number of information security threats.As an important method of characterizing the system security status,the security situation assessment is used to analyze the system security situation.However,existing situation assessment methods fail to integrate the coupling relationship between the physical layer and the information layer of the CBTC systems,and cannot dynamically characterize the real-time security situation changes under cyber attacks.In this paper,a hierarchical security situation assessment approach is proposed to address the security challenges of CBTC systems,which can perceive cyber attacks,quantify the security situation,and characterize the security situation changes under cyber attacks.Specifically,for the physical layer ofCBTC systems,the impact of cyber attacks is evaluated with the train punctuality rate and train departure interval indicators.For the information layer of CBTC systems,the system vulnerabilities and system threats are selected as static level indicators,and the critical network characteristics are selected as dynamic level indicators to quantify the real-time security situation.Finally,the comprehensive security situation assessment value of the CBTC systems is obtained by integrating the physical and information layer indicators.Simulation results illustrate that the proposed approach can dynamically characterize the real-time security situation of CBTC systems,enhancing the ability to perceive and assess information security risks.展开更多
In the era of big data,the financial industry is undergoing profound changes.By integrating multiple data sources such as transaction records,customer interactions,market trends,and regulatory requirements,big data te...In the era of big data,the financial industry is undergoing profound changes.By integrating multiple data sources such as transaction records,customer interactions,market trends,and regulatory requirements,big data technology has significantly improved the decision-making efficiency,customer insight,and risk management capabilities of financial institutions.The financial industry has become a pioneer in the application of big data technology,which is widely used in scenarios such as fraud detection,risk management,customer service optimization,and smart transactions.However,financial data security management also faces many challenges,including data breaches,privacy protection,compliance requirements,the complexity of emerging technologies,and the balance between data access and security.This article explores the major challenges of financial data security management,coping strategies,and the evolution of the regulatory environment,and it looks ahead to future trends,highlighting the important role of artificial intelligence and machine learning in financial data security.展开更多
Data compression plays a vital role in datamanagement and information theory by reducing redundancy.However,it lacks built-in security features such as secret keys or password-based access control,leaving sensitive da...Data compression plays a vital role in datamanagement and information theory by reducing redundancy.However,it lacks built-in security features such as secret keys or password-based access control,leaving sensitive data vulnerable to unauthorized access and misuse.With the exponential growth of digital data,robust security measures are essential.Data encryption,a widely used approach,ensures data confidentiality by making it unreadable and unalterable through secret key control.Despite their individual benefits,both require significant computational resources.Additionally,performing them separately for the same data increases complexity and processing time.Recognizing the need for integrated approaches that balance compression ratios and security levels,this research proposes an integrated data compression and encryption algorithm,named IDCE,for enhanced security and efficiency.Thealgorithmoperates on 128-bit block sizes and a 256-bit secret key length.It combines Huffman coding for compression and a Tent map for encryption.Additionally,an iterative Arnold cat map further enhances cryptographic confusion properties.Experimental analysis validates the effectiveness of the proposed algorithm,showcasing competitive performance in terms of compression ratio,security,and overall efficiency when compared to prior algorithms in the field.展开更多
With the rapid development of information technology, the deep integration of the financial sector and the internet has become a key driving force for economic growth. However, while this trend brings convenience, it ...With the rapid development of information technology, the deep integration of the financial sector and the internet has become a key driving force for economic growth. However, while this trend brings convenience, it also poses significant cybersecurity challenges to the financial sector. This study comprehensively analyzes the current state, challenges, and protective measures of cybersecurity in the financial sector, aiming to provide important references for financial institutions in formulating cybersecurity strategies and enhancing risk management.展开更多
In the context of the rapid development of digital education,the security of educational data has become an increasing concern.This paper explores strategies for the classification and grading of educational data,and ...In the context of the rapid development of digital education,the security of educational data has become an increasing concern.This paper explores strategies for the classification and grading of educational data,and constructs a higher educational data security management and control model centered on the integration of medical and educational data.By implementing a multi-dimensional strategy of dynamic classification,real-time authorization,and secure execution through educational data security levels,dynamic access control is applied to effectively enhance the security and controllability of educational data,providing a secure foundation for data sharing and openness.展开更多
1.Data security in smart manufacturing The global manufacturing sector is undergoing a digital transformation as traditional systems-reliant on physical assets such as raw materials and labor-struggle to meet demands ...1.Data security in smart manufacturing The global manufacturing sector is undergoing a digital transformation as traditional systems-reliant on physical assets such as raw materials and labor-struggle to meet demands for greater flexibility and efficiency.The integration of advanced information technology facilitates smart manufacturing(SM),which optimizes production,management,and supply chains[1].展开更多
A basic procedure for transforming readable data into encoded forms is encryption, which ensures security when the right decryption keys are used. Hadoop is susceptible to possible cyber-attacks because it lacks built...A basic procedure for transforming readable data into encoded forms is encryption, which ensures security when the right decryption keys are used. Hadoop is susceptible to possible cyber-attacks because it lacks built-in security measures, even though it can effectively handle and store enormous datasets using the Hadoop Distributed File System (HDFS). The increasing number of data breaches emphasizes how urgently creative encryption techniques are needed in cloud-based big data settings. This paper presents Adaptive Attribute-Based Honey Encryption (AABHE), a state-of-the-art technique that combines honey encryption with Ciphertext-Policy Attribute-Based Encryption (CP-ABE) to provide improved data security. Even if intercepted, AABHE makes sure that sensitive data cannot be accessed by unauthorized parties. With a focus on protecting huge files in HDFS, the suggested approach achieves 98% security robustness and 95% encryption efficiency, outperforming other encryption methods including Ciphertext-Policy Attribute-Based Encryption (CP-ABE), Key-Policy Attribute-Based Encryption (KB-ABE), and Advanced Encryption Standard combined with Attribute-Based Encryption (AES+ABE). By fixing Hadoop’s security flaws, AABHE fortifies its protections against data breaches and enhances Hadoop’s dependability as a platform for processing and storing massive amounts of data.展开更多
In this paper,we review and analyze intrusion detection systems for Agriculture 4.0 cyber security.Specifically,we present cyber security threats and evaluation metrics used in the performance evaluation of an intrusi...In this paper,we review and analyze intrusion detection systems for Agriculture 4.0 cyber security.Specifically,we present cyber security threats and evaluation metrics used in the performance evaluation of an intrusion detection system for Agriculture 4.0.Then,we evaluate intrusion detection systems according to emerging technologies,including,Cloud computing,Fog/Edge computing,Network virtualization,Autonomous tractors,Drones,Internet of Things,Industrial agriculture,and Smart Grids.Based on the machine learning technique used,we provide a comprehensive classification of intrusion detection systems in each emerging technology.Furthermore,we present public datasets,and the implementation frameworks applied in the performance evaluation of intrusion detection systems for Agriculture 4.0.Finally,we outline challenges and future research directions in cyber security intrusion detection for Agriculture 4.0.展开更多
The challenge of achieving situational understanding is a limiting factor in effective, timely, and adaptive cyber-security analysis. Anomaly detection fills a critical role in network assessment and trend analysis, b...The challenge of achieving situational understanding is a limiting factor in effective, timely, and adaptive cyber-security analysis. Anomaly detection fills a critical role in network assessment and trend analysis, both of which underlie the establishment of comprehensive situational understanding. To that end, we propose a cyber security data warehouse implemented as a hierarchical graph of aggregations that captures anomalies at multiple scales. Each node of our proposed graph is a summarization table of cyber event aggregations, and the edges are aggregation operators. The cyber security data warehouse enables domain experts to quickly traverse a multi-scale aggregation space systematically. We describe the architecture of a test bed system and a summary of results on the IEEE VAST 2012 Cyber Forensics data.展开更多
Many organizations have datasets which contain a high volume of personal data on individuals,e.g.,health data.Even without a name or address,persons can be identified based on the details(variables)on the dataset.This...Many organizations have datasets which contain a high volume of personal data on individuals,e.g.,health data.Even without a name or address,persons can be identified based on the details(variables)on the dataset.This is an important issue for big data holders such as public sector organizations(e.g.,Public Health Organizations)and social media companies.This paper looks at how individuals can be identified from big data using a mathematical approach and how to apply this mathematical solution to prevent accidental disclosure of a person’s details.The mathematical concept is known as the“Identity Correlation Approach”(ICA)and demonstrates how an individual can be identified without a name or address using a unique set of characteristics(variables).Secondly,having identified the individual person,it shows how a solution can be put in place to prevent accidental disclosure of the personal details.Thirdly,how to store data such that accidental leaks of the datasets do not lead to the disclosure of the personal details to unauthorized users.展开更多
Driven by the rapid development of the Internet of Things,cloud computing and other emerging technologies,the connotation of cyberspace is constantly expanding and becoming the fifth dimension of human activities.Howe...Driven by the rapid development of the Internet of Things,cloud computing and other emerging technologies,the connotation of cyberspace is constantly expanding and becoming the fifth dimension of human activities.However,security problems in cyberspace are becoming serious,and traditional defense measures(e.g.,firewall,intrusion detection systems,and security audits)often fall into a passive situation of being prone to attacks and difficult to take effect when responding to new types of network attacks with a higher and higher degree of coordination and intelligence.By constructing and implementing the diverse strategy of dynamic transformation,the configuration characteristics of systems are constantly changing,and the probability of vulnerability exposure is increasing.Therefore,the difficulty and cost of attack are increasing,which provides new ideas for reversing the asymmetric situation of defense and attack in cyberspace.Nonetheless,few related works systematically introduce dynamic defense mechanisms for cyber security.The related concepts and development strategies of dynamic defense are rarely analyzed and summarized.To bridge this gap,we conduct a comprehensive and concrete survey of recent research efforts on dynamic defense in cyber security.Specifically,we firstly introduce basic concepts and define dynamic defense in cyber security.Next,we review the architectures,enabling techniques and methods for moving target defense and mimic defense.This is followed by taxonomically summarizing the implementation and evaluation of dynamic defense.Finally,we discuss some open challenges and opportunities for dynamic defense in cyber security.展开更多
Since the publication of Satoshi Nakamoto's white paper on Bitcoin in 2008,blockchain has(slowly)become one of the most frequently discussed methods for securing data storage and transfer through decentralized,tru...Since the publication of Satoshi Nakamoto's white paper on Bitcoin in 2008,blockchain has(slowly)become one of the most frequently discussed methods for securing data storage and transfer through decentralized,trustless,peer-to-peer systems.This research identifies peer-reviewed literature that seeks to utilize blockchain for cyber security purposes and presents a systematic analysis of the most frequently adopted blockchain security applications.Our findings show that the Internet of Things(IoT)lends itself well to novel blockchain applications,as do networks and machine visualization,public-key cryptography,web applications,certification schemes and the secure storage of Personally Identifiable Information(PII).This timely systematic review also sheds light on future directions of research,education and practices in the blockchain and cyber security space,such as security of blockchain in IoT,security of blockchain for AI data,and sidechain security.展开更多
Identification of security risk factors for small reservoirs is the basis for implementation of early warning systems.The manner of identification of the factors for small reservoirs is of practical significance when ...Identification of security risk factors for small reservoirs is the basis for implementation of early warning systems.The manner of identification of the factors for small reservoirs is of practical significance when data are incomplete.The existing grey relational models have some disadvantages in measuring the correlation between categorical data sequences.To this end,this paper introduces a new grey relational model to analyze heterogeneous data.In this study,a set of security risk factors for small reservoirs was first constructed based on theoretical analysis,and heterogeneous data of these factors were recorded as sequences.The sequences were regarded as random variables,and the information entropy and conditional entropy between sequences were measured to analyze the relational degree between risk factors.Then,a new grey relational analysis model for heterogeneous data was constructed,and a comprehensive security risk factor identification method was developed.A case study of small reservoirs in Guangxi Zhuang Autonomous Region in China shows that the model constructed in this study is applicable to security risk factor identification for small reservoirs with heterogeneous and sparse data.展开更多
A hierarchical peer-to-peer（P2P）model and a data fusion method for network security situation awareness system are proposed to improve the efficiency of distributed security behavior monitoring network.The single po...A hierarchical peer-to-peer（P2P）model and a data fusion method for network security situation awareness system are proposed to improve the efficiency of distributed security behavior monitoring network.The single point failure of data analysis nodes is avoided by this P2P model,in which a greedy data forwarding method based on node priority and link delay is devised to promote the efficiency of data analysis nodes.And the data fusion method based on repulsive theory-Dumpster/Shafer（PSORT-DS）is used to deal with the challenge of multi-source alarm information.This data fusion method debases the false alarm rate.Compared with improved Dumpster/Shafer（DS）theoretical method based on particle swarm optimization（PSO）and classical DS evidence theoretical method,the proposed model reduces false alarm rate by 3%and 7%,respectively,whereas their detection rate increases by 4%and 16%,respectively.展开更多
The accelerating global energy transition,driven by climate imperatives and technological advancements,demands fundamen-tal transformations in power systems.Smart grids,characterized by cyber-physical integration,dist...The accelerating global energy transition,driven by climate imperatives and technological advancements,demands fundamen-tal transformations in power systems.Smart grids,characterized by cyber-physical integration,distributed renewable resources,and data-driven intelligence,have emerged as the backbone of this evolution.This convergence,however,introduces unprecedented complexities in resilience,security,stability,and market operation.This special issue presents five pivotal studies addressing these interconnected challenges,offering novel methodologies and insights to advance the efficiency,resilience,and sustainability of modern power systems.展开更多
Cyberattacks are difficult to prevent because the targeted companies and organizations are often relying on new and fundamentally insecure cloudbased technologies,such as the Internet of Things.With increasing industr...Cyberattacks are difficult to prevent because the targeted companies and organizations are often relying on new and fundamentally insecure cloudbased technologies,such as the Internet of Things.With increasing industry adoption and migration of traditional computing services to the cloud,one of the main challenges in cybersecurity is to provide mechanisms to secure these technologies.This work proposes a Data Security Framework for cloud computing services(CCS)that evaluates and improves CCS data security from a software engineering perspective by evaluating the levels of security within the cloud computing paradigm using engineering methods and techniques applied to CCS.This framework is developed by means of a methodology based on a heuristic theory that incorporates knowledge generated by existing works as well as the experience of their implementation.The paper presents the design details of the framework,which consists of three stages:identification of data security requirements,management of data security risks and evaluation of data security performance in CCS.展开更多
As the Internet of Things (IoT) is emerging as an attractive paradigm, a typical IoT architecture that U2IoT (Unit IoT and Ubiquitous IoT) model has been presented for the future IoT. Based on the U2IoT model, this pa...As the Internet of Things (IoT) is emerging as an attractive paradigm, a typical IoT architecture that U2IoT (Unit IoT and Ubiquitous IoT) model has been presented for the future IoT. Based on the U2IoT model, this paper proposes a cyber-physical-social based security architecture (IPM) to deal with Information, Physical, and Management security perspectives, and presents how the architectural abstractions support U2IoT model. In particular, 1) an information security model is established to describe the mapping relations among U2IoT, security layer, and security requirement, in which social layer and additional intelligence and compatibility properties are infused into IPM;2) physical security referring to the external context and inherent infrastructure are inspired by artificial immune algorithms;3) recommended security strategies are suggested for social management control. The proposed IPM combining the cyber world, physical world and human social provides constructive proposal towards the future IoT security and privacy protection.展开更多
文摘Climate change,natural disasters,pollution,and fast urbanization have made environmental security a more serious international issue.Timely,accurate,and multi-dimensional information is essential in the effective monitoring and management of such complex challenges in the environment.The Earth Observation(EO)systems,including optical sensors,radar sensors,Light Detection and Ranging(LiDAR)sensors,thermal sensors,Unmanned Aerial Vehicle(UAV)sensors,and in-situ sensors,offer a good coverage of space and time,as well as provide useful information on land,water,and atmospheric processes.But the shortcomings or weaknesses of individual sensors,such as their vulnerability to weather conditions,spectral or spatial resolution,and gaps in time,can tend to limit their ability to provide a complete picture of the environment.One of the solutions has been multi-sensor fusion,which combines heterogeneous data and makes it more accurate,robust,and interpretable.This systematic review analyzes the latest methods of multi-sensor fusion,which are machine learning,deep learning,probabilistic models,and hybrid approaches,in terms of methodological principles,preprocessing needs,and computational frameworks.Applications in environmental security are highlighted,which include monitoring natural disasters,monitoring of climate and ecosystem,pollution monitoring,monitoring of land use change,and early warning systems.The review also covers evaluation measures,validation plans,and uncertainty measures,where a strict measure of evaluation is vital to making actionable decisions.Lastly,emerging issues,e.g.,data heterogeneity,computational needs,sensor interoperability,and prospects in the future,e.g.,AI-based adaptive fusion,UAVs and Internet of Things(IoT)integration,and scalable cloud-based systems,are discussed.The synthesis has highlighted the transformational capability of multi-sensor EO in terms of improving the environment in the context of environmental security and sustainable management.
文摘The accelerated advancement of the Internet of Things(IoT)has generated substantial data,including sensitive and private information.Consequently,it is imperative to guarantee the security of data sharing.While facilitating fine-grained access control,Ciphertext Policy Attribute-Based Encryption(CP-ABE)can effectively ensure the confidentiality of shared data.Nevertheless,the conventional centralized CP-ABE scheme is plagued by the issues of keymisuse,key escrow,and large computation,which will result in security risks.This paper suggests a lightweight IoT data security sharing scheme that integrates blockchain technology and CP-ABE to address the abovementioned issues.The integrity and traceability of shared data are guaranteed by the use of blockchain technology to store and verify access transactions.The encryption and decryption operations of the CP-ABE algorithm have been implemented using elliptic curve scalarmultiplication to accommodate lightweight IoT devices,as opposed to themore arithmetic bilinear pairing found in the traditional CP-ABE algorithm.Additionally,a portion of the computation is delegated to the edge nodes to alleviate the computational burden on users.A distributed key management method is proposed to address the issues of key escrow andmisuse.Thismethod employs the edge blockchain to facilitate the storage and distribution of attribute private keys.Meanwhile,data security sharing is enhanced by combining off-chain and on-chain ciphertext storage.The security and performance analysis indicates that the proposed scheme is more efficient and secure.
基金supported by the National Key Research and Development Program of China(2022YFB2703503)the National Natural Science Foundation of China(62293501,62525210,and 62293502)the China Scholarship Council(202306280318).
文摘1.Introduction Data inference(DInf)is a data security threat in which critical information is inferred from low-sensitivity data.Once regarded as an advanced professional threat limited to intelligence analysts,DInf has become a widespread risk in the artificial intelligence(AI)era.
基金supported in part by the project of the State Key Laboratory of Advanced Rail Autonomous Operation(RAO2023ZZ004)in part by the Beijing Natural Science Foundation-Fengtai Rail Transit Frontier Research Joint Fund(L211002)+2 种基金in part by the Foundation of China State Railway Group Corporation Limited under Grant L2021G003in part by the Scientific and Technical Research Fund of China Academy of Railway Sciences Corporation Limited under Grant 2021YJ094in part by the Project I23L00200 and Project I24F00010.
文摘With the integration of informatization and intelligence into the Communication-Based Train Control(CBTC)systems,the system is facing an increasing number of information security threats.As an important method of characterizing the system security status,the security situation assessment is used to analyze the system security situation.However,existing situation assessment methods fail to integrate the coupling relationship between the physical layer and the information layer of the CBTC systems,and cannot dynamically characterize the real-time security situation changes under cyber attacks.In this paper,a hierarchical security situation assessment approach is proposed to address the security challenges of CBTC systems,which can perceive cyber attacks,quantify the security situation,and characterize the security situation changes under cyber attacks.Specifically,for the physical layer ofCBTC systems,the impact of cyber attacks is evaluated with the train punctuality rate and train departure interval indicators.For the information layer of CBTC systems,the system vulnerabilities and system threats are selected as static level indicators,and the critical network characteristics are selected as dynamic level indicators to quantify the real-time security situation.Finally,the comprehensive security situation assessment value of the CBTC systems is obtained by integrating the physical and information layer indicators.Simulation results illustrate that the proposed approach can dynamically characterize the real-time security situation of CBTC systems,enhancing the ability to perceive and assess information security risks.
基金Exploration and Practice of the Application of Blockchain Technology to the Cultivation of Compound Talents under the Background of Free Trade Port(HKJG2023-18)。
文摘In the era of big data,the financial industry is undergoing profound changes.By integrating multiple data sources such as transaction records,customer interactions,market trends,and regulatory requirements,big data technology has significantly improved the decision-making efficiency,customer insight,and risk management capabilities of financial institutions.The financial industry has become a pioneer in the application of big data technology,which is widely used in scenarios such as fraud detection,risk management,customer service optimization,and smart transactions.However,financial data security management also faces many challenges,including data breaches,privacy protection,compliance requirements,the complexity of emerging technologies,and the balance between data access and security.This article explores the major challenges of financial data security management,coping strategies,and the evolution of the regulatory environment,and it looks ahead to future trends,highlighting the important role of artificial intelligence and machine learning in financial data security.
基金the Deanship of Graduate Studies and Scientific Research at Qassim University for financial support(QU-APC-2025).
文摘Data compression plays a vital role in datamanagement and information theory by reducing redundancy.However,it lacks built-in security features such as secret keys or password-based access control,leaving sensitive data vulnerable to unauthorized access and misuse.With the exponential growth of digital data,robust security measures are essential.Data encryption,a widely used approach,ensures data confidentiality by making it unreadable and unalterable through secret key control.Despite their individual benefits,both require significant computational resources.Additionally,performing them separately for the same data increases complexity and processing time.Recognizing the need for integrated approaches that balance compression ratios and security levels,this research proposes an integrated data compression and encryption algorithm,named IDCE,for enhanced security and efficiency.Thealgorithmoperates on 128-bit block sizes and a 256-bit secret key length.It combines Huffman coding for compression and a Tent map for encryption.Additionally,an iterative Arnold cat map further enhances cryptographic confusion properties.Experimental analysis validates the effectiveness of the proposed algorithm,showcasing competitive performance in terms of compression ratio,security,and overall efficiency when compared to prior algorithms in the field.
文摘With the rapid development of information technology, the deep integration of the financial sector and the internet has become a key driving force for economic growth. However, while this trend brings convenience, it also poses significant cybersecurity challenges to the financial sector. This study comprehensively analyzes the current state, challenges, and protective measures of cybersecurity in the financial sector, aiming to provide important references for financial institutions in formulating cybersecurity strategies and enhancing risk management.
基金supported by:the 2023 Basic Public Welfare Research Project of the Wenzhou Science and Technology Bureau“Research on Multi-Source Data Classification and Grading Standards and Intelligent Algorithms for Higher Education Institutions”(Project No.G2023094)Major Humanities and Social Sciences Research Projects in Zhejiang higher education institutions(Grant/Award Number:2024QN061)2023 Basic Public Welfare Research Project of Wenzhou(No.:S2023014).
文摘In the context of the rapid development of digital education,the security of educational data has become an increasing concern.This paper explores strategies for the classification and grading of educational data,and constructs a higher educational data security management and control model centered on the integration of medical and educational data.By implementing a multi-dimensional strategy of dynamic classification,real-time authorization,and secure execution through educational data security levels,dynamic access control is applied to effectively enhance the security and controllability of educational data,providing a secure foundation for data sharing and openness.
基金supported in part by the National Natural Science Foundation of China(62293511 and 62402256)in part by the Shandong Provincial Natural Science Foundation of China(ZR2024MF100)+1 种基金in part by the Taishan Scholars Program(tsqn202408239)in part by the Open Research Project of the State Key Laboratory of Industrial Control Technology,Zhejiang University,China(ICT2025B13).
文摘1.Data security in smart manufacturing The global manufacturing sector is undergoing a digital transformation as traditional systems-reliant on physical assets such as raw materials and labor-struggle to meet demands for greater flexibility and efficiency.The integration of advanced information technology facilitates smart manufacturing(SM),which optimizes production,management,and supply chains[1].
基金funded by Princess Nourah bint Abdulrahman UniversityResearchers Supporting Project number (PNURSP2024R408), Princess Nourah bint AbdulrahmanUniversity, Riyadh, Saudi Arabia.
文摘A basic procedure for transforming readable data into encoded forms is encryption, which ensures security when the right decryption keys are used. Hadoop is susceptible to possible cyber-attacks because it lacks built-in security measures, even though it can effectively handle and store enormous datasets using the Hadoop Distributed File System (HDFS). The increasing number of data breaches emphasizes how urgently creative encryption techniques are needed in cloud-based big data settings. This paper presents Adaptive Attribute-Based Honey Encryption (AABHE), a state-of-the-art technique that combines honey encryption with Ciphertext-Policy Attribute-Based Encryption (CP-ABE) to provide improved data security. Even if intercepted, AABHE makes sure that sensitive data cannot be accessed by unauthorized parties. With a focus on protecting huge files in HDFS, the suggested approach achieves 98% security robustness and 95% encryption efficiency, outperforming other encryption methods including Ciphertext-Policy Attribute-Based Encryption (CP-ABE), Key-Policy Attribute-Based Encryption (KB-ABE), and Advanced Encryption Standard combined with Attribute-Based Encryption (AES+ABE). By fixing Hadoop’s security flaws, AABHE fortifies its protections against data breaches and enhances Hadoop’s dependability as a platform for processing and storing massive amounts of data.
基金supported in part by the Research Start-Up Fund for Talent Researcher of Nanjing Agricultural University(77H0603)in part by the National Natural Science Foundation of China(62072248)。
文摘In this paper,we review and analyze intrusion detection systems for Agriculture 4.0 cyber security.Specifically,we present cyber security threats and evaluation metrics used in the performance evaluation of an intrusion detection system for Agriculture 4.0.Then,we evaluate intrusion detection systems according to emerging technologies,including,Cloud computing,Fog/Edge computing,Network virtualization,Autonomous tractors,Drones,Internet of Things,Industrial agriculture,and Smart Grids.Based on the machine learning technique used,we provide a comprehensive classification of intrusion detection systems in each emerging technology.Furthermore,we present public datasets,and the implementation frameworks applied in the performance evaluation of intrusion detection systems for Agriculture 4.0.Finally,we outline challenges and future research directions in cyber security intrusion detection for Agriculture 4.0.
文摘The challenge of achieving situational understanding is a limiting factor in effective, timely, and adaptive cyber-security analysis. Anomaly detection fills a critical role in network assessment and trend analysis, both of which underlie the establishment of comprehensive situational understanding. To that end, we propose a cyber security data warehouse implemented as a hierarchical graph of aggregations that captures anomalies at multiple scales. Each node of our proposed graph is a summarization table of cyber event aggregations, and the edges are aggregation operators. The cyber security data warehouse enables domain experts to quickly traverse a multi-scale aggregation space systematically. We describe the architecture of a test bed system and a summary of results on the IEEE VAST 2012 Cyber Forensics data.
文摘Many organizations have datasets which contain a high volume of personal data on individuals,e.g.,health data.Even without a name or address,persons can be identified based on the details(variables)on the dataset.This is an important issue for big data holders such as public sector organizations(e.g.,Public Health Organizations)and social media companies.This paper looks at how individuals can be identified from big data using a mathematical approach and how to apply this mathematical solution to prevent accidental disclosure of a person’s details.The mathematical concept is known as the“Identity Correlation Approach”(ICA)and demonstrates how an individual can be identified without a name or address using a unique set of characteristics(variables).Secondly,having identified the individual person,it shows how a solution can be put in place to prevent accidental disclosure of the personal details.Thirdly,how to store data such that accidental leaks of the datasets do not lead to the disclosure of the personal details to unauthorized users.
基金supported by the Financial and Science Technology Plan Project of Xinjiang Production and Construction Corps,under grants No.2020DB005 and No.2017DB005supported by the Priority Academic Program Development of Jiangsu Higher Education Institutions fund.
文摘Driven by the rapid development of the Internet of Things,cloud computing and other emerging technologies,the connotation of cyberspace is constantly expanding and becoming the fifth dimension of human activities.However,security problems in cyberspace are becoming serious,and traditional defense measures(e.g.,firewall,intrusion detection systems,and security audits)often fall into a passive situation of being prone to attacks and difficult to take effect when responding to new types of network attacks with a higher and higher degree of coordination and intelligence.By constructing and implementing the diverse strategy of dynamic transformation,the configuration characteristics of systems are constantly changing,and the probability of vulnerability exposure is increasing.Therefore,the difficulty and cost of attack are increasing,which provides new ideas for reversing the asymmetric situation of defense and attack in cyberspace.Nonetheless,few related works systematically introduce dynamic defense mechanisms for cyber security.The related concepts and development strategies of dynamic defense are rarely analyzed and summarized.To bridge this gap,we conduct a comprehensive and concrete survey of recent research efforts on dynamic defense in cyber security.Specifically,we firstly introduce basic concepts and define dynamic defense in cyber security.Next,we review the architectures,enabling techniques and methods for moving target defense and mimic defense.This is followed by taxonomically summarizing the implementation and evaluation of dynamic defense.Finally,we discuss some open challenges and opportunities for dynamic defense in cyber security.
文摘Since the publication of Satoshi Nakamoto's white paper on Bitcoin in 2008,blockchain has(slowly)become one of the most frequently discussed methods for securing data storage and transfer through decentralized,trustless,peer-to-peer systems.This research identifies peer-reviewed literature that seeks to utilize blockchain for cyber security purposes and presents a systematic analysis of the most frequently adopted blockchain security applications.Our findings show that the Internet of Things(IoT)lends itself well to novel blockchain applications,as do networks and machine visualization,public-key cryptography,web applications,certification schemes and the secure storage of Personally Identifiable Information(PII).This timely systematic review also sheds light on future directions of research,education and practices in the blockchain and cyber security space,such as security of blockchain in IoT,security of blockchain for AI data,and sidechain security.
基金supported by the National Nature Science Foundation of China(Grant No.71401052)the National Social Science Foundation of China(Grant No.17BGL156)the Key Project of the National Social Science Foundation of China(Grant No.14AZD024)
文摘Identification of security risk factors for small reservoirs is the basis for implementation of early warning systems.The manner of identification of the factors for small reservoirs is of practical significance when data are incomplete.The existing grey relational models have some disadvantages in measuring the correlation between categorical data sequences.To this end,this paper introduces a new grey relational model to analyze heterogeneous data.In this study,a set of security risk factors for small reservoirs was first constructed based on theoretical analysis,and heterogeneous data of these factors were recorded as sequences.The sequences were regarded as random variables,and the information entropy and conditional entropy between sequences were measured to analyze the relational degree between risk factors.Then,a new grey relational analysis model for heterogeneous data was constructed,and a comprehensive security risk factor identification method was developed.A case study of small reservoirs in Guangxi Zhuang Autonomous Region in China shows that the model constructed in this study is applicable to security risk factor identification for small reservoirs with heterogeneous and sparse data.
基金Supported by the National Natural Science Foundation of China(61370212)the Research Fund for the Doctoral Program of Higher Education of China(20122304130002)+1 种基金the Natural Science Foundation of Heilongjiang Province(ZD 201102)the Fundamental Research Fund for the Central Universities(HEUCFZ1213,HEUCF100601)
文摘A hierarchical peer-to-peer（P2P）model and a data fusion method for network security situation awareness system are proposed to improve the efficiency of distributed security behavior monitoring network.The single point failure of data analysis nodes is avoided by this P2P model,in which a greedy data forwarding method based on node priority and link delay is devised to promote the efficiency of data analysis nodes.And the data fusion method based on repulsive theory-Dumpster/Shafer（PSORT-DS）is used to deal with the challenge of multi-source alarm information.This data fusion method debases the false alarm rate.Compared with improved Dumpster/Shafer（DS）theoretical method based on particle swarm optimization（PSO）and classical DS evidence theoretical method,the proposed model reduces false alarm rate by 3%and 7%,respectively,whereas their detection rate increases by 4%and 16%,respectively.
文摘The accelerating global energy transition,driven by climate imperatives and technological advancements,demands fundamen-tal transformations in power systems.Smart grids,characterized by cyber-physical integration,distributed renewable resources,and data-driven intelligence,have emerged as the backbone of this evolution.This convergence,however,introduces unprecedented complexities in resilience,security,stability,and market operation.This special issue presents five pivotal studies addressing these interconnected challenges,offering novel methodologies and insights to advance the efficiency,resilience,and sustainability of modern power systems.
文摘Cyberattacks are difficult to prevent because the targeted companies and organizations are often relying on new and fundamentally insecure cloudbased technologies,such as the Internet of Things.With increasing industry adoption and migration of traditional computing services to the cloud,one of the main challenges in cybersecurity is to provide mechanisms to secure these technologies.This work proposes a Data Security Framework for cloud computing services(CCS)that evaluates and improves CCS data security from a software engineering perspective by evaluating the levels of security within the cloud computing paradigm using engineering methods and techniques applied to CCS.This framework is developed by means of a methodology based on a heuristic theory that incorporates knowledge generated by existing works as well as the experience of their implementation.The paper presents the design details of the framework,which consists of three stages:identification of data security requirements,management of data security risks and evaluation of data security performance in CCS.
文摘As the Internet of Things (IoT) is emerging as an attractive paradigm, a typical IoT architecture that U2IoT (Unit IoT and Ubiquitous IoT) model has been presented for the future IoT. Based on the U2IoT model, this paper proposes a cyber-physical-social based security architecture (IPM) to deal with Information, Physical, and Management security perspectives, and presents how the architectural abstractions support U2IoT model. In particular, 1) an information security model is established to describe the mapping relations among U2IoT, security layer, and security requirement, in which social layer and additional intelligence and compatibility properties are infused into IPM;2) physical security referring to the external context and inherent infrastructure are inspired by artificial immune algorithms;3) recommended security strategies are suggested for social management control. The proposed IPM combining the cyber world, physical world and human social provides constructive proposal towards the future IoT security and privacy protection.
