Lightweight nodes are crucial for blockchain scalability,but verifying the availability of complete block data puts significant strain on bandwidth and latency.Existing data availability sampling(DAS)schemes either re...Lightweight nodes are crucial for blockchain scalability,but verifying the availability of complete block data puts significant strain on bandwidth and latency.Existing data availability sampling(DAS)schemes either require trusted setups or suffer from high communication overhead and low verification efficiency.This paper presents ISTIRDA,a DAS scheme that lets light clients certify availability by sampling small random codeword symbols.Built on ISTIR,an improved Reed–Solomon interactive oracle proof of proximity,ISTIRDA combines adaptive folding with dynamic code rate adjustment to preserve soundness while lowering communication.This paper formalizes opening consistency and prove security with bounded error in the random oracle model,giving polylogarithmic verifier queries and no trusted setup.In a prototype compared with FRIDA under equal soundness,ISTIRDA reduces communication by 40.65%to 80%.For data larger than 16 MB,ISTIRDA verifies faster and the advantage widens;at 128 MB,proofs are about 60%smaller and verification time is roughly 25%shorter,while prover overhead remains modest.In peer-to-peer emulation under injected latency and loss,ISTIRDA reaches confidence more quickly and is less sensitive to packet loss and load.These results indicate that ISTIRDA is a scalable and provably secure DAS scheme suitable for high-throughput,large-block public blockchains,substantially easing bandwidth and latency pressure on lightweight nodes.展开更多
The exponential growth of the Internet of Things(IoT)has introduced significant security challenges,with zero-day attacks emerging as one of the most critical and challenging threats.Traditional Machine Learning(ML)an...The exponential growth of the Internet of Things(IoT)has introduced significant security challenges,with zero-day attacks emerging as one of the most critical and challenging threats.Traditional Machine Learning(ML)and Deep Learning(DL)techniques have demonstrated promising early detection capabilities.However,their effectiveness is limited when handling the vast volumes of IoT-generated data due to scalability constraints,high computational costs,and the costly time-intensive process of data labeling.To address these challenges,this study proposes a Federated Learning(FL)framework that leverages collaborative and hybrid supervised learning to enhance cyber threat detection in IoT networks.By employing Deep Neural Networks(DNNs)and decentralized model training,the approach reduces computational complexity while improving detection accuracy.The proposed model demonstrates robust performance,achieving accuracies of 94.34%,99.95%,and 87.94%on the publicly available kitsune,Bot-IoT,and UNSW-NB15 datasets,respectively.Furthermore,its ability to detect zero-day attacks is validated through evaluations on two additional benchmark datasets,TON-IoT and IoT-23,using a Deep Federated Learning(DFL)framework,underscoring the generalization and effectiveness of the model in heterogeneous and decentralized IoT environments.Experimental results demonstrate superior performance over existing methods,establishing the proposed framework as an efficient and scalable solution for IoT security.展开更多
As smart grid technology rapidly advances,the vast amount of user data collected by smart meter presents significant challenges in data security and privacy protection.Current research emphasizes data security and use...As smart grid technology rapidly advances,the vast amount of user data collected by smart meter presents significant challenges in data security and privacy protection.Current research emphasizes data security and user privacy concerns within smart grids.However,existing methods struggle with efficiency and security when processing large-scale data.Balancing efficient data processing with stringent privacy protection during data aggregation in smart grids remains an urgent challenge.This paper proposes an AI-based multi-type data aggregation method designed to enhance aggregation efficiency and security by standardizing and normalizing various data modalities.The approach optimizes data preprocessing,integrates Long Short-Term Memory(LSTM)networks for handling time-series data,and employs homomorphic encryption to safeguard user privacy.It also explores the application of Boneh Lynn Shacham(BLS)signatures for user authentication.The proposed scheme’s efficiency,security,and privacy protection capabilities are validated through rigorous security proofs and experimental analysis.展开更多
The explosive expansion of the Internet of Things(IoT)systems has increased the imperative to have strong and robust solutions to cyber Security,especially to curtail Distributed Denial of Service(DDoS)attacks,which c...The explosive expansion of the Internet of Things(IoT)systems has increased the imperative to have strong and robust solutions to cyber Security,especially to curtail Distributed Denial of Service(DDoS)attacks,which can cripple critical infrastructure.The proposed framework presented in the current paper is a new hybrid scheme that induces deep learning-based traffic classification and blockchain-enabledmitigation tomake intelligent,decentralized,and real-time DDoS countermeasures in an IoT network.The proposed model fuses the extracted deep features with statistical features and trains them by using traditional machine-learning algorithms,which makes them more accurate in detection than statistical features alone,based on the Convolutional Neural Network(CNN)architecture,which can extract deep features.A permissioned blockchain will be included to record the threat cases immutably and automatically execute mitigation measures through smart contracts to provide transparency and resilience.When tested on two test sets,BoT-IoT and IoT-23,the framework obtains a maximum F1-score at 97.5 percent and only a 1.8 percent false positive rate,which compares favorably to other solutions regarding effectiveness and the amount of time required to respond.Our findings support the feasibility of our method as an extensible and secure paradigm of nextgeneration IoT security,which has constrictive utility in mission-critical or resource-constrained settings.The work is a substantial milestone in autonomous and trustful mitigation against DDoS attacks through intelligent learning and decentralized enforcement.展开更多
As an essential tool for quantitative analysis of lower limb coordination,optical motion capture systems with marker-based encoding still suffer from inefficiency,high costs,spatial constraints,and the requirement for...As an essential tool for quantitative analysis of lower limb coordination,optical motion capture systems with marker-based encoding still suffer from inefficiency,high costs,spatial constraints,and the requirement for multiple markers.While 3D pose estimation algorithms combined with ordinary cameras offer an alternative,their accuracy often deteriorates under significant body occlusion.To address the challenge of insufficient 3D pose estimation precision in occluded scenarios—which hinders the quantitative analysis of athletes’lower-limb coordination—this paper proposes a multimodal training framework integrating spatiotemporal dependency networks with text-semantic guidance.Compared to traditional optical motion capture systems,this work achieves low-cost,high-precision motion parameter acquisition through the following innovations:(1)spatiotemporal dependency attention module is designed to establish dynamic spatiotemporal correlation graphs via cross-frame joint semantic matching,effectively resolving the feature fragmentation issue in existing methods.(2)noise-suppressed multi-scale temporal module is proposed,leveraging KL divergence-based information gain analysis for progressive feature filtering in long-range dependencies,reducing errors by 1.91 mm compared to conventional temporal convolutions.(3)text-pose contrastive learning paradigm is introduced for the first time,where BERT-generated action descriptions align semantic-geometric features via the BERT encoder,significantly enhancing robustness under severe occlusion(50%joint invisibility).On the Human3.6M dataset,the proposed method achieves an MPJPE of 56.21 mm under Protocol 1,outperforming the state-of-the-art baseline MHFormer by 3.3%.Extensive ablation studies on Human3.6M demonstrate the individual contributions of the core modules:the spatiotemporal dependency module and noise-suppressed multi-scale temporal module reduce MPJPE by 0.30 and 0.34 mm,respectively,while the multimodal training strategy further decreases MPJPE by 0.6 mm through text-skeleton contrastive learning.Comparative experiments involving 16 athletes show that the sagittal plane coupling angle measurements of hip-ankle joints differ by less than 1.2°from those obtained via traditional optical systems(two one-sided t-tests,p<0.05),validating real-world reliability.This study provides an AI-powered analytical solution for competitive sports training,serving as a viable alternative to specialized equipment.展开更多
RESTful APIs have been adopted as the standard way of developing web services,allowing for smooth communication between clients and servers.Their simplicity,scalability,and compatibility have made them crucial to mode...RESTful APIs have been adopted as the standard way of developing web services,allowing for smooth communication between clients and servers.Their simplicity,scalability,and compatibility have made them crucial to modern web environments.However,the increased adoption of RESTful APIs has simultaneously exposed these interfaces to significant security threats that jeopardize the availability,confidentiality,and integrity of web services.This survey focuses exclusively on RESTful APIs,providing an in-depth perspective distinct from studies addressing other API types such as GraphQL or SOAP.We highlight concrete threats-such as injection attacks and insecure direct object references(IDOR)-to illustrate the evolving risk landscape.Our work systematically reviews state-of-the-art detection methods,including static code analysis and penetration testing,and proposes a novel taxonomy that categorizes vulnerabilities such as authentication and authorization issues.Unlike existing taxonomies focused on general web or network-level threats,our taxonomy emphasizes API-specific design flaws and operational dependencies,offering a more granular and actionable framework for RESTful API security.By critically assessing current detection methodologies and identifying key research gaps,we offer a structured framework that advances the understanding and mitigation of RESTful API vulnerabilities.Ultimately,this work aims to drive significant advancements in API security,thereby enhancing the resilience of web services against evolving cyber threats.展开更多
Situated in the southwestern Pacific,the Tonga-Kermadec subduction zone is separated into two parts by the Louisvlle Ridge Seamount Chain(LRSC),i.e.,the Tanga subduction zone and the Kermadec subduction zone.Known for...Situated in the southwestern Pacific,the Tonga-Kermadec subduction zone is separated into two parts by the Louisvlle Ridge Seamount Chain(LRSC),i.e.,the Tanga subduction zone and the Kermadec subduction zone.Known for its vigorous volcanic activity,frequent large earthquakes,rapid plate subduction,and distinctive subducting plate morphology,this subduction zone provides valuable insights into its structures,dynamics,and associated geohazards.This study compiles geological and geophysical datasets in this region,including seismicity,focal mechanisms,seismic reflection and refraction profiles,and seismic tomography,to understand the relationship between lithospheric structures of the subduction system and associated seismicity-volcanic activities.Our analysis suggests that variations in overlying sediment thickness,subduction rate,and subduction angle significantly influence the lithospheric deformation processes within the Tonga-Kermadec subduction system.Furthermore,these factors contribute to the notable differences in seismicity and volcanism observed between the Tonga subduction zone and the Kermadec subduction zone.This study enhances our understanding of plate tectonics by providing insights into the interplay between subduction dynamics and lithospheric deformation,which are crucial for analyzing geological and geophysical behaviors in similar subduction environments.展开更多
Ribonucleic Acid(RNA)contact prediction holds great significance for modeling RNA 3D structures and further understanding RNA biological functions.The rapid growth of RNA sequencing data has driven the development of ...Ribonucleic Acid(RNA)contact prediction holds great significance for modeling RNA 3D structures and further understanding RNA biological functions.The rapid growth of RNA sequencing data has driven the development of diverse computational methods for RNA contact prediction,and a benchmark evaluation of these methods remains essential.In this work,we first classified RNA contact prediction methods into statistical inference-based and neural networkbased ones.We then evaluated eight state-of-the-art methods on three test sets:a sequencediverse set,a structurally non-redundant set and a CASP RNA targets set.Our evaluation shows that for identifying non-local and long-range contacts,neural network-based methods outperform statistical inference-based ones,with SPOT-RNA-2D achieving the best performance,followed by CoCoNet and RNAcontact.However,for identifying the long-range tertiary contacts,which are vital for stabilizing RNA tertiary structure,statistical inference-based methods exhibit superior performance with GREMLIN emerging as the top performer.This work provides a comprehensive benchmarking of RNA contact prediction methods,highlighting their strengths and limitations to guide further methodological improvements and applications in RNA structure modeling.展开更多
This paper presents a high-security medical image encryption method that leverages a novel and robust sine-cosine map.The map demonstrates remarkable chaotic dynamics over a wide range of parameters.We employ nonlinea...This paper presents a high-security medical image encryption method that leverages a novel and robust sine-cosine map.The map demonstrates remarkable chaotic dynamics over a wide range of parameters.We employ nonlinear analytical tools to thoroughly investigate the dynamics of the chaotic map,which allows us to select optimal parameter configurations for the encryption process.Our findings indicate that the proposed sine-cosine map is capable of generating a rich variety of chaotic attractors,an essential characteristic for effective encryption.The encryption technique is based on bit-plane decomposition,wherein a plain image is divided into distinct bit planes.These planes are organized into two matrices:one containing the most significant bit planes and the other housing the least significant ones.The subsequent phases of chaotic confusion and diffusion utilize these matrices to enhance security.An auxiliary matrix is then generated,comprising the combined bit planes that yield the final encrypted image.Experimental results demonstrate that our proposed technique achieves a commendable level of security for safeguarding sensitive patient information in medical images.As a result,image quality is evaluated using the Structural Similarity Index(SSIM),yielding values close to zero for encrypted images and approaching one for decrypted images.Additionally,the entropy values of the encrypted images are near 8,with a Number of Pixel Change Rate(NPCR)and Unified Average Change Intensity(UACI)exceeding 99.50%and 33%,respectively.Furthermore,quantitative assessments of occlusion attacks,along with comparisons to leading algorithms,validate the integrity and efficacy of our medical image encryption approach.展开更多
Environmental transition can potentially influence cardiovascular health.Investigating the relationship between such transition and heart disease has important applications.This study uses federated learning(FL)in thi...Environmental transition can potentially influence cardiovascular health.Investigating the relationship between such transition and heart disease has important applications.This study uses federated learning(FL)in this context and investigates the link between climate change and heart disease.The dataset containing environmental,meteorological,and health-related factors like blood sugar,cholesterol,maximum heart rate,fasting ECG,etc.,is used with machine learning models to identify hidden patterns and relationships.Algorithms such as federated learning,XGBoost,random forest,support vector classifier,extra tree classifier,k-nearest neighbor,and logistic regression are used.A framework for diagnosing heart disease is designed using FL along with other models.Experiments involve discriminating healthy subjects from those who are heart patients and obtain an accuracy of 94.03%.The proposed FL-based framework proves to be superior to existing techniques in terms of usability,dependability,and accuracy.This study paves the way for screening people for early heart disease detection and continuous monitoring in telemedicine and remote care.Personalized treatment can also be planned with customized therapies.展开更多
In the context of an increasingly severe cybersecurity landscape and the growing complexity of offensive and defen-sive techniques,Zero Trust Networks(ZTN)have emerged as a widely recognized technology.Zero Trust not ...In the context of an increasingly severe cybersecurity landscape and the growing complexity of offensive and defen-sive techniques,Zero Trust Networks(ZTN)have emerged as a widely recognized technology.Zero Trust not only addresses the shortcomings of traditional perimeter security models but also consistently follows the fundamental principle of“never trust,always verify.”Initially proposed by John Cortez in 2010 and subsequently promoted by Google,the Zero Trust model has become a key approach to addressing the ever-growing security threats in complex network environments.This paper systematically compares the current mainstream cybersecurity models,thoroughly explores the advantages and limitations of the Zero Trust model,and provides an in-depth review of its components and key technologies.Additionally,it analyzes the latest research achievements in the application of Zero Trust technology across various fields,including network security,6G networks,the Internet of Things(IoT),and cloud computing,in the context of specific use cases.The paper also discusses the innovative contributions of the Zero Trust model in these fields,the challenges it faces,and proposes corresponding solutions and future research directions.展开更多
Urdu,a prominent subcontinental language,serves as a versatile means of communication.However,its handwritten expressions present challenges for optical character recognition(OCR).While various OCR techniques have bee...Urdu,a prominent subcontinental language,serves as a versatile means of communication.However,its handwritten expressions present challenges for optical character recognition(OCR).While various OCR techniques have been proposed,most of them focus on recognizing printed Urdu characters and digits.To the best of our knowledge,very little research has focused solely on Urdu pure handwriting recognition,and the results of such proposed methods are often inadequate.In this study,we introduce a novel approach to recognizing Urdu pure handwritten digits and characters using Convolutional Neural Networks(CNN).Our proposed method utilizes convolutional layers to extract important features from input images and classifies them using fully connected layers,enabling efficient and accurate detection of Urdu handwritten digits and characters.We implemented the proposed technique on a large publicly available dataset of Urdu handwritten digits and characters.The findings demonstrate that the CNN model achieves an accuracy of 98.30%and an F1 score of 88.6%,indicating its effectiveness in detecting and classifyingUrdu handwritten digits and characters.These results have far-reaching implications for various applications,including document analysis,text recognition,and language understanding,which have previously been unexplored in the context of Urdu handwriting data.This work lays a solid foundation for future research and development in Urdu language detection and processing,opening up new opportunities for advancement in this field.展开更多
Secure and automated sharing of medical information among different medical entities/stakeholders like patients,hospitals,doctors,law enforcement agencies,health insurance companies etc.,in a standard format has alway...Secure and automated sharing of medical information among different medical entities/stakeholders like patients,hospitals,doctors,law enforcement agencies,health insurance companies etc.,in a standard format has always been a challenging problem.Current methods for ensuring compliance with medical privacy laws require specialists who are deeply familiar with these laws'complex requirements to verify the lawful exchange of medical information.This article introduces a Smart Medical Data Exchange Engine(SDEE)designed to automate the extracting of logical rules from medical privacy legislation using advanced techniques.These rules facilitate the secure extraction of information,safeguarding patient privacy and confidentiality.In addition,SMDEE can generate standardised clinical documents according to Health Level 7(HL7)standards and also standardise the nomenclature of requested medical data,enabling accurate decision-making when accessing patient data.All access requests to patient information are processed through SMDEE to ensure authorised access.The proposed system's efficacy is evaluated using the Health Insurance Portability and Accountability Act(HIPAA),a fundamental privacy law in the United States.However,SMDEE's flexibility allows its application worldwide,accommodating various medical privacy laws.Beyond facilitating global information exchange,SMDEE aims to enhance international patients'timely and appropriate treatment.展开更多
This paper deals with the problem of distributed formation tracking control and obstacle avoidance of multivehicle systems(MVSs)in complex obstacle-laden environments.The MVS under consideration consists of a leader v...This paper deals with the problem of distributed formation tracking control and obstacle avoidance of multivehicle systems(MVSs)in complex obstacle-laden environments.The MVS under consideration consists of a leader vehicle with an unknown control input and a group of follower vehicles,connected via a directed interaction topology,subject to simultaneous unknown heterogeneous nonlinearities and external disturbances.The central aim is to achieve effective and collisionfree formation tracking control for the nonlinear and uncertain MVS with obstacles encountered in formation maneuvering,while not demanding global information of the interaction topology.Toward this goal,a radial basis function neural network is used to model the unknown nonlinearity of vehicle dynamics in each vehicle and repulsive potentials are employed for obstacle avoidance.Furthermore,a scalable distributed adaptive formation tracking control protocol with a built-in obstacle avoidance mechanism is developed.It is proved that,with the proposed protocol,the resulting formation tracking errors are uniformly ultimately bounded and obstacle collision avoidance is guaranteed.Comprehensive simulation results are elaborated to substantiate the effectiveness and the promising collision avoidance performance of the proposed scalable adaptive formation control approach.展开更多
Inverse sensing is an important research direction to provide new perspectives for optical sensing. For inverse sensing, the primary challenge is that scattered photon has a complicated profile, which is hard to deriv...Inverse sensing is an important research direction to provide new perspectives for optical sensing. For inverse sensing, the primary challenge is that scattered photon has a complicated profile, which is hard to derive a general solution. Instead of a general solution, it is more feasible and practical to derive a solution based on a specific environment. With deep learning, we develop a multifunctional inverse sensing approach for a specific environment. This inverse sensing approach can reconstruct the information of scattered photons and characterize multiple optical parameters simultaneously. Its functionality can be upgraded dynamically after learning more data. It has wide measurement range and can characterize the optical signals behind obstructions. The high anti-noise performance, flexible implementation, and extremely high threshold to optical damage or saturation make it useful for a wide range of applications, including self-driving car, space technology, data security, biological characterization, and integrated photonics.展开更多
This paper presents a method for tracing a planar implicit curve f（x, y）=0 on a rectangular region based on continuation scheme. First, according to the starting track-point and the starting track-direction of the c...This paper presents a method for tracing a planar implicit curve f（x, y）=0 on a rectangular region based on continuation scheme. First, according to the starting track-point and the starting track-direction of the curve, make a new fimction F（x, y）=0 where the same curve withf（x, y）=0 is defined. Then we trace the curve between the two domains where F（x, y）〉0 and F（x, y）〈0 alternately, according to the two rules presented in this paper. Equal step size or adaptive step size can be used, when we trace the curve. An irregular planar implicit curve （such as the curve with large curvatures at some points on the curve）, can be plotted if an adaptive step size is used. Moreover, this paper presents a scheme to search for the multiple points on the curve. Our method has the following advantages： （1） it can plot Co planar implicit curves; （2） it can plot the planar implicit curves with multiple points; （3） by the help of using the two rules, our method does not need to compute the tangent vector at the points on the curve, and directly searches for the direction of the tracing curve; （4） the tracing procedure costs only one of two evaluations of function f（x, y）=0 per moving step, while most existing similar methods cost more evaluations of the function.展开更多
基金supported in part by the Research Fund of Key Lab of Education Blockchain and Intelligent Technology,Ministry of Education(EBME25-F-08).
文摘Lightweight nodes are crucial for blockchain scalability,but verifying the availability of complete block data puts significant strain on bandwidth and latency.Existing data availability sampling(DAS)schemes either require trusted setups or suffer from high communication overhead and low verification efficiency.This paper presents ISTIRDA,a DAS scheme that lets light clients certify availability by sampling small random codeword symbols.Built on ISTIR,an improved Reed–Solomon interactive oracle proof of proximity,ISTIRDA combines adaptive folding with dynamic code rate adjustment to preserve soundness while lowering communication.This paper formalizes opening consistency and prove security with bounded error in the random oracle model,giving polylogarithmic verifier queries and no trusted setup.In a prototype compared with FRIDA under equal soundness,ISTIRDA reduces communication by 40.65%to 80%.For data larger than 16 MB,ISTIRDA verifies faster and the advantage widens;at 128 MB,proofs are about 60%smaller and verification time is roughly 25%shorter,while prover overhead remains modest.In peer-to-peer emulation under injected latency and loss,ISTIRDA reaches confidence more quickly and is less sensitive to packet loss and load.These results indicate that ISTIRDA is a scalable and provably secure DAS scheme suitable for high-throughput,large-block public blockchains,substantially easing bandwidth and latency pressure on lightweight nodes.
基金supported by Princess Nourah bint Abdulrahman University Researchers Supporting Project Number(PNURSP2025R97)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘The exponential growth of the Internet of Things(IoT)has introduced significant security challenges,with zero-day attacks emerging as one of the most critical and challenging threats.Traditional Machine Learning(ML)and Deep Learning(DL)techniques have demonstrated promising early detection capabilities.However,their effectiveness is limited when handling the vast volumes of IoT-generated data due to scalability constraints,high computational costs,and the costly time-intensive process of data labeling.To address these challenges,this study proposes a Federated Learning(FL)framework that leverages collaborative and hybrid supervised learning to enhance cyber threat detection in IoT networks.By employing Deep Neural Networks(DNNs)and decentralized model training,the approach reduces computational complexity while improving detection accuracy.The proposed model demonstrates robust performance,achieving accuracies of 94.34%,99.95%,and 87.94%on the publicly available kitsune,Bot-IoT,and UNSW-NB15 datasets,respectively.Furthermore,its ability to detect zero-day attacks is validated through evaluations on two additional benchmark datasets,TON-IoT and IoT-23,using a Deep Federated Learning(DFL)framework,underscoring the generalization and effectiveness of the model in heterogeneous and decentralized IoT environments.Experimental results demonstrate superior performance over existing methods,establishing the proposed framework as an efficient and scalable solution for IoT security.
基金supported by the National Key R&D Program of China(No.2023YFB2703700)the National Natural Science Foundation of China(Nos.U21A20465,62302457,62402444,62172292)+4 种基金the Fundamental Research Funds of Zhejiang Sci-Tech University(Nos.23222092-Y,22222266-Y)the Program for Leading Innovative Research Team of Zhejiang Province(No.2023R01001)the Zhejiang Provincial Natural Science Foundation of China(Nos.LQ24F020008,LQ24F020012)the Foundation of State Key Laboratory of Public Big Data(No.[2022]417)the“Pioneer”and“Leading Goose”R&D Program of Zhejiang(No.2023C01119).
文摘As smart grid technology rapidly advances,the vast amount of user data collected by smart meter presents significant challenges in data security and privacy protection.Current research emphasizes data security and user privacy concerns within smart grids.However,existing methods struggle with efficiency and security when processing large-scale data.Balancing efficient data processing with stringent privacy protection during data aggregation in smart grids remains an urgent challenge.This paper proposes an AI-based multi-type data aggregation method designed to enhance aggregation efficiency and security by standardizing and normalizing various data modalities.The approach optimizes data preprocessing,integrates Long Short-Term Memory(LSTM)networks for handling time-series data,and employs homomorphic encryption to safeguard user privacy.It also explores the application of Boneh Lynn Shacham(BLS)signatures for user authentication.The proposed scheme’s efficiency,security,and privacy protection capabilities are validated through rigorous security proofs and experimental analysis.
文摘The explosive expansion of the Internet of Things(IoT)systems has increased the imperative to have strong and robust solutions to cyber Security,especially to curtail Distributed Denial of Service(DDoS)attacks,which can cripple critical infrastructure.The proposed framework presented in the current paper is a new hybrid scheme that induces deep learning-based traffic classification and blockchain-enabledmitigation tomake intelligent,decentralized,and real-time DDoS countermeasures in an IoT network.The proposed model fuses the extracted deep features with statistical features and trains them by using traditional machine-learning algorithms,which makes them more accurate in detection than statistical features alone,based on the Convolutional Neural Network(CNN)architecture,which can extract deep features.A permissioned blockchain will be included to record the threat cases immutably and automatically execute mitigation measures through smart contracts to provide transparency and resilience.When tested on two test sets,BoT-IoT and IoT-23,the framework obtains a maximum F1-score at 97.5 percent and only a 1.8 percent false positive rate,which compares favorably to other solutions regarding effectiveness and the amount of time required to respond.Our findings support the feasibility of our method as an extensible and secure paradigm of nextgeneration IoT security,which has constrictive utility in mission-critical or resource-constrained settings.The work is a substantial milestone in autonomous and trustful mitigation against DDoS attacks through intelligent learning and decentralized enforcement.
基金supported by the Major Sports Research Projects of Jiangsu Provincial Sports Bureau in 2022(No.ST221101).
文摘As an essential tool for quantitative analysis of lower limb coordination,optical motion capture systems with marker-based encoding still suffer from inefficiency,high costs,spatial constraints,and the requirement for multiple markers.While 3D pose estimation algorithms combined with ordinary cameras offer an alternative,their accuracy often deteriorates under significant body occlusion.To address the challenge of insufficient 3D pose estimation precision in occluded scenarios—which hinders the quantitative analysis of athletes’lower-limb coordination—this paper proposes a multimodal training framework integrating spatiotemporal dependency networks with text-semantic guidance.Compared to traditional optical motion capture systems,this work achieves low-cost,high-precision motion parameter acquisition through the following innovations:(1)spatiotemporal dependency attention module is designed to establish dynamic spatiotemporal correlation graphs via cross-frame joint semantic matching,effectively resolving the feature fragmentation issue in existing methods.(2)noise-suppressed multi-scale temporal module is proposed,leveraging KL divergence-based information gain analysis for progressive feature filtering in long-range dependencies,reducing errors by 1.91 mm compared to conventional temporal convolutions.(3)text-pose contrastive learning paradigm is introduced for the first time,where BERT-generated action descriptions align semantic-geometric features via the BERT encoder,significantly enhancing robustness under severe occlusion(50%joint invisibility).On the Human3.6M dataset,the proposed method achieves an MPJPE of 56.21 mm under Protocol 1,outperforming the state-of-the-art baseline MHFormer by 3.3%.Extensive ablation studies on Human3.6M demonstrate the individual contributions of the core modules:the spatiotemporal dependency module and noise-suppressed multi-scale temporal module reduce MPJPE by 0.30 and 0.34 mm,respectively,while the multimodal training strategy further decreases MPJPE by 0.6 mm through text-skeleton contrastive learning.Comparative experiments involving 16 athletes show that the sagittal plane coupling angle measurements of hip-ankle joints differ by less than 1.2°from those obtained via traditional optical systems(two one-sided t-tests,p<0.05),validating real-world reliability.This study provides an AI-powered analytical solution for competitive sports training,serving as a viable alternative to specialized equipment.
文摘RESTful APIs have been adopted as the standard way of developing web services,allowing for smooth communication between clients and servers.Their simplicity,scalability,and compatibility have made them crucial to modern web environments.However,the increased adoption of RESTful APIs has simultaneously exposed these interfaces to significant security threats that jeopardize the availability,confidentiality,and integrity of web services.This survey focuses exclusively on RESTful APIs,providing an in-depth perspective distinct from studies addressing other API types such as GraphQL or SOAP.We highlight concrete threats-such as injection attacks and insecure direct object references(IDOR)-to illustrate the evolving risk landscape.Our work systematically reviews state-of-the-art detection methods,including static code analysis and penetration testing,and proposes a novel taxonomy that categorizes vulnerabilities such as authentication and authorization issues.Unlike existing taxonomies focused on general web or network-level threats,our taxonomy emphasizes API-specific design flaws and operational dependencies,offering a more granular and actionable framework for RESTful API security.By critically assessing current detection methodologies and identifying key research gaps,we offer a structured framework that advances the understanding and mitigation of RESTful API vulnerabilities.Ultimately,this work aims to drive significant advancements in API security,thereby enhancing the resilience of web services against evolving cyber threats.
基金supported by Special Projects in Universities’Key Fields of Guangdong Province(No.2023ZDZX3017)the 2022 Tertiary Education Scientific Research Project of Guangzhou Municipal Education Bureau(No.202234607)+1 种基金the Guangdong Basic and Applied Basic Research Foundation(No.2025A1515012983)the National Natural Science Foundation of China(Nos.52371059 and 52101358).
文摘Situated in the southwestern Pacific,the Tonga-Kermadec subduction zone is separated into two parts by the Louisvlle Ridge Seamount Chain(LRSC),i.e.,the Tanga subduction zone and the Kermadec subduction zone.Known for its vigorous volcanic activity,frequent large earthquakes,rapid plate subduction,and distinctive subducting plate morphology,this subduction zone provides valuable insights into its structures,dynamics,and associated geohazards.This study compiles geological and geophysical datasets in this region,including seismicity,focal mechanisms,seismic reflection and refraction profiles,and seismic tomography,to understand the relationship between lithospheric structures of the subduction system and associated seismicity-volcanic activities.Our analysis suggests that variations in overlying sediment thickness,subduction rate,and subduction angle significantly influence the lithospheric deformation processes within the Tonga-Kermadec subduction system.Furthermore,these factors contribute to the notable differences in seismicity and volcanism observed between the Tonga subduction zone and the Kermadec subduction zone.This study enhances our understanding of plate tectonics by providing insights into the interplay between subduction dynamics and lithospheric deformation,which are crucial for analyzing geological and geophysical behaviors in similar subduction environments.
基金supported by grants from the National Natural Science Foundation of China(Grant No.12205223 to YLT,Grant No.12375038 to ZJT and Grant No.11605125 to YZS)the Department of Education of Hubei Province(Grant No.Q20221705 to YLT)。
文摘Ribonucleic Acid(RNA)contact prediction holds great significance for modeling RNA 3D structures and further understanding RNA biological functions.The rapid growth of RNA sequencing data has driven the development of diverse computational methods for RNA contact prediction,and a benchmark evaluation of these methods remains essential.In this work,we first classified RNA contact prediction methods into statistical inference-based and neural networkbased ones.We then evaluated eight state-of-the-art methods on three test sets:a sequencediverse set,a structurally non-redundant set and a CASP RNA targets set.Our evaluation shows that for identifying non-local and long-range contacts,neural network-based methods outperform statistical inference-based ones,with SPOT-RNA-2D achieving the best performance,followed by CoCoNet and RNAcontact.However,for identifying the long-range tertiary contacts,which are vital for stabilizing RNA tertiary structure,statistical inference-based methods exhibit superior performance with GREMLIN emerging as the top performer.This work provides a comprehensive benchmarking of RNA contact prediction methods,highlighting their strengths and limitations to guide further methodological improvements and applications in RNA structure modeling.
文摘This paper presents a high-security medical image encryption method that leverages a novel and robust sine-cosine map.The map demonstrates remarkable chaotic dynamics over a wide range of parameters.We employ nonlinear analytical tools to thoroughly investigate the dynamics of the chaotic map,which allows us to select optimal parameter configurations for the encryption process.Our findings indicate that the proposed sine-cosine map is capable of generating a rich variety of chaotic attractors,an essential characteristic for effective encryption.The encryption technique is based on bit-plane decomposition,wherein a plain image is divided into distinct bit planes.These planes are organized into two matrices:one containing the most significant bit planes and the other housing the least significant ones.The subsequent phases of chaotic confusion and diffusion utilize these matrices to enhance security.An auxiliary matrix is then generated,comprising the combined bit planes that yield the final encrypted image.Experimental results demonstrate that our proposed technique achieves a commendable level of security for safeguarding sensitive patient information in medical images.As a result,image quality is evaluated using the Structural Similarity Index(SSIM),yielding values close to zero for encrypted images and approaching one for decrypted images.Additionally,the entropy values of the encrypted images are near 8,with a Number of Pixel Change Rate(NPCR)and Unified Average Change Intensity(UACI)exceeding 99.50%and 33%,respectively.Furthermore,quantitative assessments of occlusion attacks,along with comparisons to leading algorithms,validate the integrity and efficacy of our medical image encryption approach.
基金funded by Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2025R104),Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘Environmental transition can potentially influence cardiovascular health.Investigating the relationship between such transition and heart disease has important applications.This study uses federated learning(FL)in this context and investigates the link between climate change and heart disease.The dataset containing environmental,meteorological,and health-related factors like blood sugar,cholesterol,maximum heart rate,fasting ECG,etc.,is used with machine learning models to identify hidden patterns and relationships.Algorithms such as federated learning,XGBoost,random forest,support vector classifier,extra tree classifier,k-nearest neighbor,and logistic regression are used.A framework for diagnosing heart disease is designed using FL along with other models.Experiments involve discriminating healthy subjects from those who are heart patients and obtain an accuracy of 94.03%.The proposed FL-based framework proves to be superior to existing techniques in terms of usability,dependability,and accuracy.This study paves the way for screening people for early heart disease detection and continuous monitoring in telemedicine and remote care.Personalized treatment can also be planned with customized therapies.
基金supported by the National Natural Science Foundation of China(Grants Nos.62473146,62072249 and 62072056)the National Science Foundation of Hunan Province(Grant No.2024JJ3017)+1 种基金the Hunan Provincial Key Research and Development Program(Grant No.2022GK2019)by the Researchers Supporting Project Number(RSP2024R509),King Saud University,Riyadh,Saudi Arabia.
文摘In the context of an increasingly severe cybersecurity landscape and the growing complexity of offensive and defen-sive techniques,Zero Trust Networks(ZTN)have emerged as a widely recognized technology.Zero Trust not only addresses the shortcomings of traditional perimeter security models but also consistently follows the fundamental principle of“never trust,always verify.”Initially proposed by John Cortez in 2010 and subsequently promoted by Google,the Zero Trust model has become a key approach to addressing the ever-growing security threats in complex network environments.This paper systematically compares the current mainstream cybersecurity models,thoroughly explores the advantages and limitations of the Zero Trust model,and provides an in-depth review of its components and key technologies.Additionally,it analyzes the latest research achievements in the application of Zero Trust technology across various fields,including network security,6G networks,the Internet of Things(IoT),and cloud computing,in the context of specific use cases.The paper also discusses the innovative contributions of the Zero Trust model in these fields,the challenges it faces,and proposes corresponding solutions and future research directions.
文摘Urdu,a prominent subcontinental language,serves as a versatile means of communication.However,its handwritten expressions present challenges for optical character recognition(OCR).While various OCR techniques have been proposed,most of them focus on recognizing printed Urdu characters and digits.To the best of our knowledge,very little research has focused solely on Urdu pure handwriting recognition,and the results of such proposed methods are often inadequate.In this study,we introduce a novel approach to recognizing Urdu pure handwritten digits and characters using Convolutional Neural Networks(CNN).Our proposed method utilizes convolutional layers to extract important features from input images and classifies them using fully connected layers,enabling efficient and accurate detection of Urdu handwritten digits and characters.We implemented the proposed technique on a large publicly available dataset of Urdu handwritten digits and characters.The findings demonstrate that the CNN model achieves an accuracy of 98.30%and an F1 score of 88.6%,indicating its effectiveness in detecting and classifyingUrdu handwritten digits and characters.These results have far-reaching implications for various applications,including document analysis,text recognition,and language understanding,which have previously been unexplored in the context of Urdu handwriting data.This work lays a solid foundation for future research and development in Urdu language detection and processing,opening up new opportunities for advancement in this field.
基金fully supported by the University of Vaasa and VTT Technical Research Centre of Finland.
文摘Secure and automated sharing of medical information among different medical entities/stakeholders like patients,hospitals,doctors,law enforcement agencies,health insurance companies etc.,in a standard format has always been a challenging problem.Current methods for ensuring compliance with medical privacy laws require specialists who are deeply familiar with these laws'complex requirements to verify the lawful exchange of medical information.This article introduces a Smart Medical Data Exchange Engine(SDEE)designed to automate the extracting of logical rules from medical privacy legislation using advanced techniques.These rules facilitate the secure extraction of information,safeguarding patient privacy and confidentiality.In addition,SMDEE can generate standardised clinical documents according to Health Level 7(HL7)standards and also standardise the nomenclature of requested medical data,enabling accurate decision-making when accessing patient data.All access requests to patient information are processed through SMDEE to ensure authorised access.The proposed system's efficacy is evaluated using the Health Insurance Portability and Accountability Act(HIPAA),a fundamental privacy law in the United States.However,SMDEE's flexibility allows its application worldwide,accommodating various medical privacy laws.Beyond facilitating global information exchange,SMDEE aims to enhance international patients'timely and appropriate treatment.
文摘This paper deals with the problem of distributed formation tracking control and obstacle avoidance of multivehicle systems(MVSs)in complex obstacle-laden environments.The MVS under consideration consists of a leader vehicle with an unknown control input and a group of follower vehicles,connected via a directed interaction topology,subject to simultaneous unknown heterogeneous nonlinearities and external disturbances.The central aim is to achieve effective and collisionfree formation tracking control for the nonlinear and uncertain MVS with obstacles encountered in formation maneuvering,while not demanding global information of the interaction topology.Toward this goal,a radial basis function neural network is used to model the unknown nonlinearity of vehicle dynamics in each vehicle and repulsive potentials are employed for obstacle avoidance.Furthermore,a scalable distributed adaptive formation tracking control protocol with a built-in obstacle avoidance mechanism is developed.It is proved that,with the proposed protocol,the resulting formation tracking errors are uniformly ultimately bounded and obstacle collision avoidance is guaranteed.Comprehensive simulation results are elaborated to substantiate the effectiveness and the promising collision avoidance performance of the proposed scalable adaptive formation control approach.
文摘Inverse sensing is an important research direction to provide new perspectives for optical sensing. For inverse sensing, the primary challenge is that scattered photon has a complicated profile, which is hard to derive a general solution. Instead of a general solution, it is more feasible and practical to derive a solution based on a specific environment. With deep learning, we develop a multifunctional inverse sensing approach for a specific environment. This inverse sensing approach can reconstruct the information of scattered photons and characterize multiple optical parameters simultaneously. Its functionality can be upgraded dynamically after learning more data. It has wide measurement range and can characterize the optical signals behind obstructions. The high anti-noise performance, flexible implementation, and extremely high threshold to optical damage or saturation make it useful for a wide range of applications, including self-driving car, space technology, data security, biological characterization, and integrated photonics.
文摘This paper presents a method for tracing a planar implicit curve f（x, y）=0 on a rectangular region based on continuation scheme. First, according to the starting track-point and the starting track-direction of the curve, make a new fimction F（x, y）=0 where the same curve withf（x, y）=0 is defined. Then we trace the curve between the two domains where F（x, y）〉0 and F（x, y）〈0 alternately, according to the two rules presented in this paper. Equal step size or adaptive step size can be used, when we trace the curve. An irregular planar implicit curve （such as the curve with large curvatures at some points on the curve）, can be plotted if an adaptive step size is used. Moreover, this paper presents a scheme to search for the multiple points on the curve. Our method has the following advantages： （1） it can plot Co planar implicit curves; （2） it can plot the planar implicit curves with multiple points; （3） by the help of using the two rules, our method does not need to compute the tangent vector at the points on the curve, and directly searches for the direction of the tracing curve; （4） the tracing procedure costs only one of two evaluations of function f（x, y）=0 per moving step, while most existing similar methods cost more evaluations of the function.
