Modern intrusion detection systems(MIDS)face persistent challenges in coping with the rapid evolution of cyber threats,high-volume network traffic,and imbalanced datasets.Traditional models often lack the robustness a...Modern intrusion detection systems(MIDS)face persistent challenges in coping with the rapid evolution of cyber threats,high-volume network traffic,and imbalanced datasets.Traditional models often lack the robustness and explainability required to detect novel and sophisticated attacks effectively.This study introduces an advanced,explainable machine learning framework for multi-class IDS using the KDD99 and IDS datasets,which reflects real-world network behavior through a blend of normal and diverse attack classes.The methodology begins with sophisticated data preprocessing,incorporating both RobustScaler and QuantileTransformer to address outliers and skewed feature distributions,ensuring standardized and model-ready inputs.Critical dimensionality reduction is achieved via the Harris Hawks Optimization(HHO)algorithm—a nature-inspired metaheuristic modeled on hawks’hunting strategies.HHO efficiently identifies the most informative features by optimizing a fitness function based on classification performance.Following feature selection,the SMOTE is applied to the training data to resolve class imbalance by synthetically augmenting underrepresented attack types.The stacked architecture is then employed,combining the strengths of XGBoost,SVM,and RF as base learners.This layered approach improves prediction robustness and generalization by balancing bias and variance across diverse classifiers.The model was evaluated using standard classification metrics:precision,recall,F1-score,and overall accuracy.The best overall performance was recorded with an accuracy of 99.44%for UNSW-NB15,demonstrating the model’s effectiveness.After balancing,the model demonstrated a clear improvement in detecting the attacks.We tested the model on four datasets to show the effectiveness of the proposed approach and performed the ablation study to check the effect of each parameter.Also,the proposed model is computationaly efficient.To support transparency and trust in decision-making,explainable AI(XAI)techniques are incorporated that provides both global and local insight into feature contributions,and offers intuitive visualizations for individual predictions.This makes it suitable for practical deployment in cybersecurity environments that demand both precision and accountability.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
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
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
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
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
[目的/意义]梳理国际国家安全情报研究发展脉络与知识生产特征,揭示关键学者的群体画像、职业发展模式、合作网络结构与核心研究议题演进,以期为推动我国安全情报学科建设提供借鉴。[方法/过程]基于发文量标准,从Intelligence and Natio...[目的/意义]梳理国际国家安全情报研究发展脉络与知识生产特征,揭示关键学者的群体画像、职业发展模式、合作网络结构与核心研究议题演进,以期为推动我国安全情报学科建设提供借鉴。[方法/过程]基于发文量标准,从Intelligence and National Security期刊中筛选出核心著者,运用履历分析法将国外核心著者履历划分为学科背景、研究方向、科研成果和工作经历4个核心类属进行比较分析,采用LDA主题模型对发文进行主题挖掘,系统识别出情报研究者关注的核心议题。[结果/结论]核心著者群体呈现显著的男性主导、中老年资深学者为主、机构高度集中、学科背景偏重传统人文社科的特征;安全情报研究面临跨学科深度融合不足、学界与实践存在隔阂、技术伦理与法律探讨滞后等问题。展开更多
Big data has ushered in an era of unprecedented access to vast amounts of new,unstructured data,particularly in the realm of sensitive information.It presents unique opportunities for enhancing risk alerting systems,b...Big data has ushered in an era of unprecedented access to vast amounts of new,unstructured data,particularly in the realm of sensitive information.It presents unique opportunities for enhancing risk alerting systems,but also poses challenges in terms of extraction and analysis due to its diverse file formats.This paper proposes the utilization of a DAE-based(Deep Auto-encoders)model for projecting risk associated with financial data.The research delves into the development of an indicator assessing the degree to which organizations successfully avoid displaying bias in handling financial information.Simulation results demonstrate the superior performance of the DAE algorithm,showcasing fewer false positives,improved overall detection rates,and a noteworthy 9%reduction in failure jitter.The optimized DAE algorithm achieves an accuracy of 99%,surpassing existing methods,thereby presenting a robust solution for sensitive data risk projection.展开更多
This research presents an analysis of smart grid units to enhance connected units’security during data transmissions.The major advantage of the proposed method is that the system model encompasses multiple aspects su...This research presents an analysis of smart grid units to enhance connected units’security during data transmissions.The major advantage of the proposed method is that the system model encompasses multiple aspects such as network flow monitoring,data expansion,control association,throughput,and losses.In addition,all the above-mentioned aspects are carried out with neural networks and adaptive optimizations to enhance the operation of smart grid networks.Moreover,the quantitative analysis of the optimization algorithm is discussed concerning two case studies,thereby achieving early convergence at reduced complexities.The suggested method ensures that each communication unit has its own distinct channels,maximizing the possibility of accurate measurements.This results in the provision of only the original data values,hence enhancing security.Both power and line values are individually observed to establish control in smart grid-connected channels,even in the presence of adaptive settings.A comparison analysis is conducted to showcase the results,using simulation studies involving four scenarios and two case studies.The proposed method exhibits reduced complexity,resulting in a throughput gain of over 90%.展开更多
Coastal Bangladesh is highly vulnerable to various impacts of climate change,including rising temperatures,unpredictable precipitation,cyclones,droughts,and saltwater intrusion.These factors collectively threaten agri...Coastal Bangladesh is highly vulnerable to various impacts of climate change,including rising temperatures,unpredictable precipitation,cyclones,droughts,and saltwater intrusion.These factors collectively threaten agricultural productivity and food security.This study examines the relationship between farmers’perceptions and observable climatic trends,with a focus on the sustainability of food systems and the promotion of adaptable farming techniques in Bagerhat District,Bangladesh.A mixed-methods strategy was employed,incorporating household surveys(a total of 110 purposively selected farmers),focus group discussions,key informant interviews,and climatic data analysis.The Mann-Kendall test,Sen’s slope estimator,precipitation concentration index(PCI),and standardized rainfall anomaly index(SRAI)were employed to analyze climate trends from 1991 to 2020.The findings showed that more than 70.00%of respondents indicated that summers were becoming warmer,over 50.00%reported that winters were becoming colder,and 63.00%stated that yearly precipitation was decreasing.Farmers reported an increase in flood occurrences and a decline in the predictability of precipitation.Between 2011 and 2019,the output of most rice varieties decreased,with the exception of high-yielding Aman rice and hybrid Boro rice.The results also showed that 60.00%of respondents reported experiencing salinity intrusion,and 57.00%attributed significant yield losses to salinity.Planting salt-tolerant rice varieties(such as BRRI Dhan 67 and Binadhan-10),practicing homestead vegetable cultivation,and moderately integrating shrimp aquaculture were also common adaptive measures.To improve long-term food security in coastal Bangladesh,we suggest growing more salt-tolerant crop varieties,promoting vertical and homestead gardening,enhancing seed systems that are resilient to climate change,and educating farmers on the use of climate-smart farming methods.This study highlights the importance of aligning farmers’perceptions with observed climatic data to design effective adaptation strategies.The findings of this study can guide policy-makers and development practitioners in strengthening climate-resilient agriculture and ensuring long-term food security in coastal Bangladesh.展开更多
This study introduces an Edge-Based Data Hiding and Extraction Algorithm(EBDHEA)to address the problem of data embedding in images while preserving robust security and high image quality.The algorithm produces three c...This study introduces an Edge-Based Data Hiding and Extraction Algorithm(EBDHEA)to address the problem of data embedding in images while preserving robust security and high image quality.The algorithm produces three classes of pixels from the pixels in the cover image:edges found by the Canny edge detection method,pixels arising from the expansion of neighboring edge pixels,and pixels that are neither edges nor components of the neighboring edge pixels.The number of Least Significant Bits(LSBs)that are used to hide data depends on these classifications.Furthermore,the lossless compression method,Huffman coding,improves image data capacity.To increase the security of the steganographic process,secret messages are encrypted using the XOR encryption technique before being embedded.Metrics such as the Mean Squared Error(MSE),Peak Signal-to-Noise Ratio(PSNR),and Structural Similarity Index Measure(SSIM)are used to assess the efficacy of this algorithm and are compared to previous methods.The findings demonstrate that the suggested approach achieves high similarity between the original and modified images with a maximum PSNR of 60.7 dB for a payload of 18,750 bytes,a maximum SSIM of 0.999 for a payload of 314,572.8 bytes,and a maximum Video Information Fidelity(VIF)of 0.95 for a payload of 23,592 bytes.Normalized Cross-Correlation(NCC)values are very close to 1.In addition,the performance of EBDHEA is implemented on Secure Medical Image Transmission as a real-world example,and the performance is tested against three types of attacks:RS Steganalysis,Chi-square attack,and visual attack,and compared with two deep learning models,such as SRNet and XuNet.展开更多
Earthquakes are highly destructive spatio-temporal phenomena whose analysis is essential for disaster preparedness and risk mitigation.Modern seismological research produces vast volumes of heterogeneous data from sei...Earthquakes are highly destructive spatio-temporal phenomena whose analysis is essential for disaster preparedness and risk mitigation.Modern seismological research produces vast volumes of heterogeneous data from seismic networks,satellite observations,and geospatial repositories,creating the need for scalable infrastructures capable of integrating and analyzing such data to support intelligent decision-making.Data warehousing technologies provide a robust foundation for this purpose;however,existing earthquake-oriented data warehouses remain limited,often relying on simplified schemas,domain-specific analytics,or cataloguing efforts.This paper presents the design and implementation of a spatio-temporal data warehouse for seismic activity.The framework integrates spatial and temporal dimensions in a unified schema and introduces a novel array-based approach for managing many-to-many relationships between facts and dimensions without intermediate bridge tables.A comparative evaluation against a conventional bridge-table schema demonstrates that the array-based design improves fact-centric query performance,while the bridge-table schema remains advantageous for dimension-centric queries.To reconcile these trade-offs,a hybrid schema is proposed that retains both representations,ensuring balanced efficiency across heterogeneous workloads.The proposed framework demonstrates how spatio-temporal data warehousing can address schema complexity,improve query performance,and support multidimensional visualization.In doing so,it provides a foundation for integrating seismic analysis into broader big data-driven intelligent decision systems for disaster resilience,risk mitigation,and emergency management.展开更多
Purpose-Amidst an increasingly severe cybersecurity landscape,the widespread adoption of Xinchuang endpoints has become a strategic imperative.Governments and enterprises have established terminal localization as a cr...Purpose-Amidst an increasingly severe cybersecurity landscape,the widespread adoption of Xinchuang endpoints has become a strategic imperative.Governments and enterprises have established terminal localization as a critical objective,aiming for comprehensive indigenous replacement through rapid technological iteration.Consequently,Xinchuang systems and Windows platforms are expected to coexist over an extended period.This study seeks to establish an automated verification framework for multi-version operating systems and validate the efficacy of baseline hardening in mitigating security risks.Design/methodology/approach-Based on the Classified Protection 2.0 framework and relevant national standards for endpoint security,this study proposes an endpoint security baseline verification scheme applicable to multiple operating systems.The scheme addresses divergent security policies and implementation methodologies across heterogeneous environments.It automates the inspection of core baselines,including account password complexity,default shared service status and patch installation status.Furthermore,a comprehensive scoring model is established by incorporating differentiated weights for account security,patch management and log auditing,ultimately generating visualized risk reports to facilitate remediation prioritization.Findings-This study reveals that baseline configuration serves as the fundamental prerequisite in endpoint security practices.Through a scalable detection engine and quantitative scoring model,the system can promptly identify and remediate potential risks,thereby reducing the attack surface and mitigating intrusion risks.However,on certain domestic chip architectures,compatibility issues persist in detecting specific configuration items.Further improvement in hardware-software co-adaptation for domestic platforms is required to advance the development of localized security protection systems.Originality/value-Through in-depth research on security baseline configurations across multiple operating systems,this study implements an automated and visualized baseline verification methodology.This approach significantly strengthens the security posture of domestic operating systems and supports the establishment of a more robust,national-level cybersecurity defense framework.展开更多
5G-R is the main type of next-generation mobile communication system for railways,offering highly reliable broadband data transmission services for intelligent railway operations.In the light of meeting the bearing de...5G-R is the main type of next-generation mobile communication system for railways,offering highly reliable broadband data transmission services for intelligent railway operations.In the light of meeting the bearing demands of the 5G-R network,a comprehensive data transmission platform is proposed.This platform enables unified accession for various data service systems and applies Software Defined Network(SDN)technology for dynamic routing selection and high-effective data forwarding.Based on shared key lightweight access authentication technology,two-way identity authentication is performed for mobile terminals and network-side devices,ensuring the legitimacy verification of heterogeneous terminals within the application domain.展开更多
This paper introduces federated services as a smart service ecology with federated security to align distributed data supply with diversified service demands spanning digital and societal contexts.It presents the comp...This paper introduces federated services as a smart service ecology with federated security to align distributed data supply with diversified service demands spanning digital and societal contexts.It presents the comprehensive researches on the theoretical foundation and technical system of federated services,aiming at advancing our understanding and implementation of this novel service paradigm.First,a thorough examination of the characteristics of federated security within federated services is conducted.Then,a five-layer technical framework is formulated under a decentralized intelligent architecture,ensuring secure,agile,and adaptable service provision.On this basis,the operational mechanisms underlying data federation and service confederation is analyzed,with emphasis on the smart supply-demand matching model.Furthermore,a scenario-oriented taxonomy of federated services accompanied by illustrative examples is proposed.Our work offers actionable insights and roadmap for realizing and advancing federated services,contributing to the refinement and wider adoption of this transformative service paradigm in the digital era.展开更多
Malnutrition remains a significant global challenge,particularly in developing countries.Policymakers have increasingly focused on improving household food security and nutrition through farm production diversity(FPD)...Malnutrition remains a significant global challenge,particularly in developing countries.Policymakers have increasingly focused on improving household food security and nutrition through farm production diversity(FPD).While research indicates that FPD correlates positively with reduced malnutrition,other studies emphasize the importance of market access for improved nutritional outcomes.However,this evidence varies by region and remains inconsistent.To address this knowledge gap,this study analyzed survey data from 450 smallholder farmers in Punjab,Pakistan,using regression models to examine the relationship between FPD and dietary diversity,as well as the underlying impact pathways.The findings demonstrate that FPD significantly correlates with increased household dietary diversity score(HDDS).FPD influences dietary diversification through both own-farm production and market food consumption pathways,with the ownfarm production pathway showing greater impact.The increase in food expenditure through own-farm production yielded a marginal return of 8% in household dietary diversity compared to 5.3% through marketing.Gender differences emerged as significant,with male-headed households showing relatively lower dietary diversity.These findings have substantial implications for countries with smallholder farming systems,providing valuable insights for the formation of agricultural policies,resource optimization,and rural development initiatives.展开更多
Andrew Wangota,a 48-year-old Ugandan farmer,has been using agrivoltaics technology,a solar technology that uses agricultural land for both food production and solar power generation,on his farm in Bunashimolo Parish,B...Andrew Wangota,a 48-year-old Ugandan farmer,has been using agrivoltaics technology,a solar technology that uses agricultural land for both food production and solar power generation,on his farm in Bunashimolo Parish,Bukyiende Subcounty in Uganda where he has been cultivating plantain,coffee and Irish potatoes for the past 16 years.展开更多
The advent of quantum computing poses a significant challenge to traditional cryptographic protocols,particularly those used in SecureMultiparty Computation(MPC),a fundamental cryptographic primitive for privacypreser...The advent of quantum computing poses a significant challenge to traditional cryptographic protocols,particularly those used in SecureMultiparty Computation(MPC),a fundamental cryptographic primitive for privacypreserving computation.Classical MPC relies on cryptographic techniques such as homomorphic encryption,secret sharing,and oblivious transfer,which may become vulnerable in the post-quantum era due to the computational power of quantum adversaries.This study presents a review of 140 peer-reviewed articles published between 2000 and 2025 that used different databases like MDPI,IEEE Explore,Springer,and Elsevier,examining the applications,types,and security issues with the solution of Quantum computing in different fields.This review explores the impact of quantum computing on MPC security,assesses emerging quantum-resistant MPC protocols,and examines hybrid classicalquantum approaches aimed at mitigating quantum threats.We analyze the role of Quantum Key Distribution(QKD),post-quantum cryptography(PQC),and quantum homomorphic encryption in securing multiparty computations.Additionally,we discuss the challenges of scalability,computational efficiency,and practical deployment of quantumsecure MPC frameworks in real-world applications such as privacy-preserving AI,secure blockchain transactions,and confidential data analysis.This review provides insights into the future research directions and open challenges in ensuring secure,scalable,and quantum-resistant multiparty computation.展开更多
基金funded by Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2025R104)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘Modern intrusion detection systems(MIDS)face persistent challenges in coping with the rapid evolution of cyber threats,high-volume network traffic,and imbalanced datasets.Traditional models often lack the robustness and explainability required to detect novel and sophisticated attacks effectively.This study introduces an advanced,explainable machine learning framework for multi-class IDS using the KDD99 and IDS datasets,which reflects real-world network behavior through a blend of normal and diverse attack classes.The methodology begins with sophisticated data preprocessing,incorporating both RobustScaler and QuantileTransformer to address outliers and skewed feature distributions,ensuring standardized and model-ready inputs.Critical dimensionality reduction is achieved via the Harris Hawks Optimization(HHO)algorithm—a nature-inspired metaheuristic modeled on hawks’hunting strategies.HHO efficiently identifies the most informative features by optimizing a fitness function based on classification performance.Following feature selection,the SMOTE is applied to the training data to resolve class imbalance by synthetically augmenting underrepresented attack types.The stacked architecture is then employed,combining the strengths of XGBoost,SVM,and RF as base learners.This layered approach improves prediction robustness and generalization by balancing bias and variance across diverse classifiers.The model was evaluated using standard classification metrics:precision,recall,F1-score,and overall accuracy.The best overall performance was recorded with an accuracy of 99.44%for UNSW-NB15,demonstrating the model’s effectiveness.After balancing,the model demonstrated a clear improvement in detecting the attacks.We tested the model on four datasets to show the effectiveness of the proposed approach and performed the ablation study to check the effect of each parameter.Also,the proposed model is computationaly efficient.To support transparency and trust in decision-making,explainable AI(XAI)techniques are incorporated that provides both global and local insight into feature contributions,and offers intuitive visualizations for individual predictions.This makes it suitable for practical deployment in cybersecurity environments that demand both precision and accountability.
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.
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 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].
文摘[目的/意义]梳理国际国家安全情报研究发展脉络与知识生产特征,揭示关键学者的群体画像、职业发展模式、合作网络结构与核心研究议题演进,以期为推动我国安全情报学科建设提供借鉴。[方法/过程]基于发文量标准,从Intelligence and National Security期刊中筛选出核心著者,运用履历分析法将国外核心著者履历划分为学科背景、研究方向、科研成果和工作经历4个核心类属进行比较分析,采用LDA主题模型对发文进行主题挖掘,系统识别出情报研究者关注的核心议题。[结果/结论]核心著者群体呈现显著的男性主导、中老年资深学者为主、机构高度集中、学科背景偏重传统人文社科的特征;安全情报研究面临跨学科深度融合不足、学界与实践存在隔阂、技术伦理与法律探讨滞后等问题。
文摘Big data has ushered in an era of unprecedented access to vast amounts of new,unstructured data,particularly in the realm of sensitive information.It presents unique opportunities for enhancing risk alerting systems,but also poses challenges in terms of extraction and analysis due to its diverse file formats.This paper proposes the utilization of a DAE-based(Deep Auto-encoders)model for projecting risk associated with financial data.The research delves into the development of an indicator assessing the degree to which organizations successfully avoid displaying bias in handling financial information.Simulation results demonstrate the superior performance of the DAE algorithm,showcasing fewer false positives,improved overall detection rates,and a noteworthy 9%reduction in failure jitter.The optimized DAE algorithm achieves an accuracy of 99%,surpassing existing methods,thereby presenting a robust solution for sensitive data risk projection.
基金supported by the Researchers Supporting Project number RSP2025R244,King Saud University,Riyadh,Saudi Arabia.
文摘This research presents an analysis of smart grid units to enhance connected units’security during data transmissions.The major advantage of the proposed method is that the system model encompasses multiple aspects such as network flow monitoring,data expansion,control association,throughput,and losses.In addition,all the above-mentioned aspects are carried out with neural networks and adaptive optimizations to enhance the operation of smart grid networks.Moreover,the quantitative analysis of the optimization algorithm is discussed concerning two case studies,thereby achieving early convergence at reduced complexities.The suggested method ensures that each communication unit has its own distinct channels,maximizing the possibility of accurate measurements.This results in the provision of only the original data values,hence enhancing security.Both power and line values are individually observed to establish control in smart grid-connected channels,even in the presence of adaptive settings.A comparison analysis is conducted to showcase the results,using simulation studies involving four scenarios and two case studies.The proposed method exhibits reduced complexity,resulting in a throughput gain of over 90%.
基金supported by the Research Grant of Military Institute of Science and Technology,Bangladesh。
文摘Coastal Bangladesh is highly vulnerable to various impacts of climate change,including rising temperatures,unpredictable precipitation,cyclones,droughts,and saltwater intrusion.These factors collectively threaten agricultural productivity and food security.This study examines the relationship between farmers’perceptions and observable climatic trends,with a focus on the sustainability of food systems and the promotion of adaptable farming techniques in Bagerhat District,Bangladesh.A mixed-methods strategy was employed,incorporating household surveys(a total of 110 purposively selected farmers),focus group discussions,key informant interviews,and climatic data analysis.The Mann-Kendall test,Sen’s slope estimator,precipitation concentration index(PCI),and standardized rainfall anomaly index(SRAI)were employed to analyze climate trends from 1991 to 2020.The findings showed that more than 70.00%of respondents indicated that summers were becoming warmer,over 50.00%reported that winters were becoming colder,and 63.00%stated that yearly precipitation was decreasing.Farmers reported an increase in flood occurrences and a decline in the predictability of precipitation.Between 2011 and 2019,the output of most rice varieties decreased,with the exception of high-yielding Aman rice and hybrid Boro rice.The results also showed that 60.00%of respondents reported experiencing salinity intrusion,and 57.00%attributed significant yield losses to salinity.Planting salt-tolerant rice varieties(such as BRRI Dhan 67 and Binadhan-10),practicing homestead vegetable cultivation,and moderately integrating shrimp aquaculture were also common adaptive measures.To improve long-term food security in coastal Bangladesh,we suggest growing more salt-tolerant crop varieties,promoting vertical and homestead gardening,enhancing seed systems that are resilient to climate change,and educating farmers on the use of climate-smart farming methods.This study highlights the importance of aligning farmers’perceptions with observed climatic data to design effective adaptation strategies.The findings of this study can guide policy-makers and development practitioners in strengthening climate-resilient agriculture and ensuring long-term food security in coastal Bangladesh.
文摘This study introduces an Edge-Based Data Hiding and Extraction Algorithm(EBDHEA)to address the problem of data embedding in images while preserving robust security and high image quality.The algorithm produces three classes of pixels from the pixels in the cover image:edges found by the Canny edge detection method,pixels arising from the expansion of neighboring edge pixels,and pixels that are neither edges nor components of the neighboring edge pixels.The number of Least Significant Bits(LSBs)that are used to hide data depends on these classifications.Furthermore,the lossless compression method,Huffman coding,improves image data capacity.To increase the security of the steganographic process,secret messages are encrypted using the XOR encryption technique before being embedded.Metrics such as the Mean Squared Error(MSE),Peak Signal-to-Noise Ratio(PSNR),and Structural Similarity Index Measure(SSIM)are used to assess the efficacy of this algorithm and are compared to previous methods.The findings demonstrate that the suggested approach achieves high similarity between the original and modified images with a maximum PSNR of 60.7 dB for a payload of 18,750 bytes,a maximum SSIM of 0.999 for a payload of 314,572.8 bytes,and a maximum Video Information Fidelity(VIF)of 0.95 for a payload of 23,592 bytes.Normalized Cross-Correlation(NCC)values are very close to 1.In addition,the performance of EBDHEA is implemented on Secure Medical Image Transmission as a real-world example,and the performance is tested against three types of attacks:RS Steganalysis,Chi-square attack,and visual attack,and compared with two deep learning models,such as SRNet and XuNet.
文摘Earthquakes are highly destructive spatio-temporal phenomena whose analysis is essential for disaster preparedness and risk mitigation.Modern seismological research produces vast volumes of heterogeneous data from seismic networks,satellite observations,and geospatial repositories,creating the need for scalable infrastructures capable of integrating and analyzing such data to support intelligent decision-making.Data warehousing technologies provide a robust foundation for this purpose;however,existing earthquake-oriented data warehouses remain limited,often relying on simplified schemas,domain-specific analytics,or cataloguing efforts.This paper presents the design and implementation of a spatio-temporal data warehouse for seismic activity.The framework integrates spatial and temporal dimensions in a unified schema and introduces a novel array-based approach for managing many-to-many relationships between facts and dimensions without intermediate bridge tables.A comparative evaluation against a conventional bridge-table schema demonstrates that the array-based design improves fact-centric query performance,while the bridge-table schema remains advantageous for dimension-centric queries.To reconcile these trade-offs,a hybrid schema is proposed that retains both representations,ensuring balanced efficiency across heterogeneous workloads.The proposed framework demonstrates how spatio-temporal data warehousing can address schema complexity,improve query performance,and support multidimensional visualization.In doing so,it provides a foundation for integrating seismic analysis into broader big data-driven intelligent decision systems for disaster resilience,risk mitigation,and emergency management.
基金supported by scientific research projects of China Academy of Railway Sciences Co.,Ltd.(grant no.2024YJ117).
文摘Purpose-Amidst an increasingly severe cybersecurity landscape,the widespread adoption of Xinchuang endpoints has become a strategic imperative.Governments and enterprises have established terminal localization as a critical objective,aiming for comprehensive indigenous replacement through rapid technological iteration.Consequently,Xinchuang systems and Windows platforms are expected to coexist over an extended period.This study seeks to establish an automated verification framework for multi-version operating systems and validate the efficacy of baseline hardening in mitigating security risks.Design/methodology/approach-Based on the Classified Protection 2.0 framework and relevant national standards for endpoint security,this study proposes an endpoint security baseline verification scheme applicable to multiple operating systems.The scheme addresses divergent security policies and implementation methodologies across heterogeneous environments.It automates the inspection of core baselines,including account password complexity,default shared service status and patch installation status.Furthermore,a comprehensive scoring model is established by incorporating differentiated weights for account security,patch management and log auditing,ultimately generating visualized risk reports to facilitate remediation prioritization.Findings-This study reveals that baseline configuration serves as the fundamental prerequisite in endpoint security practices.Through a scalable detection engine and quantitative scoring model,the system can promptly identify and remediate potential risks,thereby reducing the attack surface and mitigating intrusion risks.However,on certain domestic chip architectures,compatibility issues persist in detecting specific configuration items.Further improvement in hardware-software co-adaptation for domestic platforms is required to advance the development of localized security protection systems.Originality/value-Through in-depth research on security baseline configurations across multiple operating systems,this study implements an automated and visualized baseline verification methodology.This approach significantly strengthens the security posture of domestic operating systems and supports the establishment of a more robust,national-level cybersecurity defense framework.
文摘5G-R is the main type of next-generation mobile communication system for railways,offering highly reliable broadband data transmission services for intelligent railway operations.In the light of meeting the bearing demands of the 5G-R network,a comprehensive data transmission platform is proposed.This platform enables unified accession for various data service systems and applies Software Defined Network(SDN)technology for dynamic routing selection and high-effective data forwarding.Based on shared key lightweight access authentication technology,two-way identity authentication is performed for mobile terminals and network-side devices,ensuring the legitimacy verification of heterogeneous terminals within the application domain.
基金supported by the National Key Research and Development Program of China(2021YFB2104800)the National Natural Science Foundation of China(62103411,62436010,72171230)the Science and Technology Development Fund of Macao SAR(0093/2023/RIA2,0050/2020/A1).
文摘This paper introduces federated services as a smart service ecology with federated security to align distributed data supply with diversified service demands spanning digital and societal contexts.It presents the comprehensive researches on the theoretical foundation and technical system of federated services,aiming at advancing our understanding and implementation of this novel service paradigm.First,a thorough examination of the characteristics of federated security within federated services is conducted.Then,a five-layer technical framework is formulated under a decentralized intelligent architecture,ensuring secure,agile,and adaptable service provision.On this basis,the operational mechanisms underlying data federation and service confederation is analyzed,with emphasis on the smart supply-demand matching model.Furthermore,a scenario-oriented taxonomy of federated services accompanied by illustrative examples is proposed.Our work offers actionable insights and roadmap for realizing and advancing federated services,contributing to the refinement and wider adoption of this transformative service paradigm in the digital era.
基金appreciation to the National Natural Science Foundation of China(72071074)Natural Science Foundation of Hunan Province,China(2025JJ30031)for their financial support。
文摘Malnutrition remains a significant global challenge,particularly in developing countries.Policymakers have increasingly focused on improving household food security and nutrition through farm production diversity(FPD).While research indicates that FPD correlates positively with reduced malnutrition,other studies emphasize the importance of market access for improved nutritional outcomes.However,this evidence varies by region and remains inconsistent.To address this knowledge gap,this study analyzed survey data from 450 smallholder farmers in Punjab,Pakistan,using regression models to examine the relationship between FPD and dietary diversity,as well as the underlying impact pathways.The findings demonstrate that FPD significantly correlates with increased household dietary diversity score(HDDS).FPD influences dietary diversification through both own-farm production and market food consumption pathways,with the ownfarm production pathway showing greater impact.The increase in food expenditure through own-farm production yielded a marginal return of 8% in household dietary diversity compared to 5.3% through marketing.Gender differences emerged as significant,with male-headed households showing relatively lower dietary diversity.These findings have substantial implications for countries with smallholder farming systems,providing valuable insights for the formation of agricultural policies,resource optimization,and rural development initiatives.
文摘Andrew Wangota,a 48-year-old Ugandan farmer,has been using agrivoltaics technology,a solar technology that uses agricultural land for both food production and solar power generation,on his farm in Bunashimolo Parish,Bukyiende Subcounty in Uganda where he has been cultivating plantain,coffee and Irish potatoes for the past 16 years.
文摘The advent of quantum computing poses a significant challenge to traditional cryptographic protocols,particularly those used in SecureMultiparty Computation(MPC),a fundamental cryptographic primitive for privacypreserving computation.Classical MPC relies on cryptographic techniques such as homomorphic encryption,secret sharing,and oblivious transfer,which may become vulnerable in the post-quantum era due to the computational power of quantum adversaries.This study presents a review of 140 peer-reviewed articles published between 2000 and 2025 that used different databases like MDPI,IEEE Explore,Springer,and Elsevier,examining the applications,types,and security issues with the solution of Quantum computing in different fields.This review explores the impact of quantum computing on MPC security,assesses emerging quantum-resistant MPC protocols,and examines hybrid classicalquantum approaches aimed at mitigating quantum threats.We analyze the role of Quantum Key Distribution(QKD),post-quantum cryptography(PQC),and quantum homomorphic encryption in securing multiparty computations.Additionally,we discuss the challenges of scalability,computational efficiency,and practical deployment of quantumsecure MPC frameworks in real-world applications such as privacy-preserving AI,secure blockchain transactions,and confidential data analysis.This review provides insights into the future research directions and open challenges in ensuring secure,scalable,and quantum-resistant multiparty computation.
