Benefiting from the development of Federated Learning(FL)and distributed communication systems,large-scale intelligent applications become possible.Distributed devices not only provide adequate training data,but also ...Benefiting from the development of Federated Learning(FL)and distributed communication systems,large-scale intelligent applications become possible.Distributed devices not only provide adequate training data,but also cause privacy leakage and energy consumption.How to optimize the energy consumption in distributed communication systems,while ensuring the privacy of users and model accuracy,has become an urgent challenge.In this paper,we define the FL as a 3-layer architecture including users,agents and server.In order to find a balance among model training accuracy,privacy-preserving effect,and energy consumption,we design the training process of FL as game models.We use an extensive game tree to analyze the key elements that influence the players’decisions in the single game,and then find the incentive mechanism that meet the social norms through the repeated game.The experimental results show that the Nash equilibrium we obtained satisfies the laws of reality,and the proposed incentive mechanism can also promote users to submit high-quality data in FL.Following the multiple rounds of play,the incentive mechanism can help all players find the optimal strategies for energy,privacy,and accuracy of FL in distributed communication systems.展开更多
Decentralized finance(DeFi)is a general term for a series of financial products and services.It is based on blockchain technology and has attracted people’s attention because of its open,transparent,and intermediary ...Decentralized finance(DeFi)is a general term for a series of financial products and services.It is based on blockchain technology and has attracted people’s attention because of its open,transparent,and intermediary free.Among them,the DeFi ecosystem based on Ethereum-based blockchains attracts the most attention.However,the current decentralized financial system built on the Ethereum architecture has been exposed to many smart contract vulnerabilities during the last few years.Herein,we believe it is time to improve the understanding of the prevailing Ethereum-based DeFi ecosystem security issues.To that end,we investigate the Ethereum-based DeFi security issues:1)inherited from the real-world financial system,which can be solved by macro-control;2)induced by the problems of blockchain architecture,which require a better blockchain platform;3)caused by DeFi invented applications,which should be focused on during the project development.Based on that,we further discuss the current solutions and potential directions ofDeFi security.According to our research,we could provide a comprehensive vision to the research community for the improvement of Ethereum-basedDeFi ecosystem security.展开更多
Beyond-5G(B5G)aims to meet the growing demands of mobile traffic and expand the communication space.Considering that intelligent applications to B5G wireless communications will involve security issues regarding user ...Beyond-5G(B5G)aims to meet the growing demands of mobile traffic and expand the communication space.Considering that intelligent applications to B5G wireless communications will involve security issues regarding user data and operational data,this paper analyzes the maximum capacity of the multi-watermarking method for multimedia signal hiding as a means of alleviating the information security problem of B5G.The multiwatermarking process employs spread transform dither modulation.During the watermarking procedure,Gram-Schmidt orthogonalization is used to obtain the multiple spreading vectors.Consequently,multiple watermarks can be simultaneously embedded into the same position of a multimedia signal.Moreover,the multiple watermarks can be extracted without affecting one another during the extraction process.We analyze the effect of the size of the spreading vector on the unit maximum capacity,and consequently derive the theoretical relationship between the size of the spreading vector and the unit maximum capacity.A number of experiments are conducted to determine the optimal parameter values for maximum robustness on the premise of high capacity and good imperceptibility.展开更多
Blockchain technology has been extensively studied over the past decade as a foundation for decentralized information-sharing platforms due to its promising potential.Despite the success of existing blockchain archite...Blockchain technology has been extensively studied over the past decade as a foundation for decentralized information-sharing platforms due to its promising potential.Despite the success of existing blockchain architectures like Bitcoin,Ethereum,Filecoin,Hyperledger Fabric,BCOS,and BCS,current blockchain applications are still quite limited.Blockchain struggles with scenarios requiring high-speed transactions(e.g.,online markets)or large data storage(e.g.,video services)due to consensus efficiency limitations.Security restrictions pose risks to investors in blockchain-based economic systems(e.g.,DeFi),deterring current and potential investors.Privacy protection challenges make it difficult to involve sensitive data in blockchain applications.展开更多
Network Security Situation Awareness System YHSAS acquires,understands and displays the security factors which cause changes of network situation,and predicts the future development trend of these security factors.YHS...Network Security Situation Awareness System YHSAS acquires,understands and displays the security factors which cause changes of network situation,and predicts the future development trend of these security factors.YHSAS is developed for national backbone network,large network operators,large enterprises and other large-scale network.This paper describes its architecture and key technologies:Network Security Oriented Total Factor Information Collection and High-Dimensional Vector Space Analysis,Knowledge Representation and Management of Super Large-Scale Network Security,Multi-Level,Multi-Granularity and Multi-Dimensional Network Security Index Construction Method,Multi-Mode and Multi-Granularity Network Security Situation Prediction Technology,and so on.The performance tests show that YHSAS has high real-time performance and accuracy in security situation analysis and trend prediction.The system meets the demands of analysis and prediction for large-scale network security situation.展开更多
The stability of Non-Linear Feedback Shift Registers(NFSRs)plays an important role in the cryptographic security.Due to the complexity of nonlinear systems and the lack of efficient algebraic tools,the theorems relate...The stability of Non-Linear Feedback Shift Registers(NFSRs)plays an important role in the cryptographic security.Due to the complexity of nonlinear systems and the lack of efficient algebraic tools,the theorems related to the stability of NFSRs are still not well-developed.In this paper,we view the NFSR with periodic inputs as a Boolean control network.Based on the mathematical tool of semi-tensor product(STP),the Boolean network can be mapped into an algebraic form.Through these basic theories,we analyze the state space of non-autonomous NFSRs,and discuss the stability of an NFSR with periodic inputs of limited length or unlimited length.The simulation results are provided to prove the efficiency of the model.Based on these works,we can provide a method to analyze the stability of the NFSR with periodic input,including limited length and unlimited length.By this,we can efficiently reduce the computational complexity,and its efficiency is demonstrated by applying the theorem in simulations dealing with the stability of a non-autonomous NFSR.展开更多
In recent years, with the rapid development of sensing technology and deployment of various Internet of Everything devices, it becomes a crucial and practical challenge to enable real-time search queries for objects, ...In recent years, with the rapid development of sensing technology and deployment of various Internet of Everything devices, it becomes a crucial and practical challenge to enable real-time search queries for objects, data, and services in the Internet of Everything. Moreover, such efficient query processing techniques can provide strong facilitate the research on Internet of Everything security issues. By looking into the unique characteristics in the IoE application environment, such as high heterogeneity, high dynamics, and distributed, we develop a novel search engine model, and build a dynamic prediction model of the IoE sensor time series to meet the real-time requirements for the Internet of Everything search environment. We validated the accuracy and effectiveness of the dynamic prediction model using a public sensor dataset from Intel Lab.展开更多
BGP monitors are currently the main data resource of AS-level topology measurement,and the integrity of measurement result is limited to the location of such BGP monitors.However,there is currently no work to conduct ...BGP monitors are currently the main data resource of AS-level topology measurement,and the integrity of measurement result is limited to the location of such BGP monitors.However,there is currently no work to conduct a comprehensive study of the range of measurement results for a single BGP monitor.In this paper,we take the first step to describe the observed topology of each BGP monitor.To that end,we first investigate the construction and theoretical up-limit of the measured topology of a BGP monitor based on the valley-free model,then we evaluate the individual parts of the measured topology by comparing such theoretical results with the actually observed data.We find that:1)for more than 90%of the monitors,the actually observed peer-peer links merely takes a small part of all theoretical visible links;2)increasing the BGP monitors in the same AS may improve the measurement result,but with limited improvement;and 3)deploying multiple BGP monitors in different ASs can significantly improve the measurement results,but non-local BGP monitors can hardly replace the local AS BGP monitors.We also propose a metric for monitor selection optimization,and prove its effectiveness with experiment evaluation.展开更多
Programmable Logic Controllers(PLC),core of industrial control systems,is widely used in industrial control systems.The security of PLC is the key to the security of industrial control systems.Nowadays,a large number ...Programmable Logic Controllers(PLC),core of industrial control systems,is widely used in industrial control systems.The security of PLC is the key to the security of industrial control systems.Nowadays,a large number of industrial control systems are connected to the Internet which exposes the PLC equipment to the Internet,and thus raising security concerns.First of all,we introduce the basic principle of PLC in this paper.Then we analyze the PLC code security,firmware security,network security,virus vulnerability and Modbus communication protocol by reviewing the previous related work.Finally,we make a summary of the current security protection methods.展开更多
This paper attempts to estimate diagnostically relevant measure,i.e.,Arteriovenous Ratio with an improved retinal vessel classification using feature ranking strategies and multiple classifiers decision-combination sc...This paper attempts to estimate diagnostically relevant measure,i.e.,Arteriovenous Ratio with an improved retinal vessel classification using feature ranking strategies and multiple classifiers decision-combination scheme.The features exploited for retinal vessel characterization are based on statistical measures of histogram,different filter responses of images and local gradient in-formation.The feature selection process is based on two feature ranking approaches(Pearson Correlation Coefficient technique and Relief-F method)to rank the features followed by use of maximum classification accuracy of three supervised classifiers(κ-Nearest Neighbor,Support Vector Machine and Naïve Bayes)as a threshold for feature subset selection.Retinal vessels are labeled using the selected feature subset and proposed hybrid classification scheme,i.e.,decision fusion of multiple classifiers.The comparative analysis shows an increase in vessel classification accuracy as well as Arteriovenous Ratio calculation performance.The system is tested on three databases,a local dataset of 44 images and two publically available databases,INSPIRE-AVR containing 40 images and VICAVR containing 58 images.The local database also contains images with pathologically diseased structures.The performance of the proposed system is assessed by comparing the experimental results with the gold standard estimations as well as with the results of previous methodologies.Overall,an accuracy of 90.45%,93.90%and 87.82%is achieved in retinal blood vessel separation with 0.0565,0.0650 and 0.0849 mean error in Arte-riovenous Ratio calculation for Local,INSPIRE-AVR and VICAVR dataset,respectively.展开更多
In recent years,with the great success of pre-trained language models,the pre-trained BERT model has been gradually applied to the field of source code understanding.However,the time cost of training a language model ...In recent years,with the great success of pre-trained language models,the pre-trained BERT model has been gradually applied to the field of source code understanding.However,the time cost of training a language model from zero is very high,and how to transfer the pre-trained language model to the field of smart contract vulnerability detection is a hot research direction at present.In this paper,we propose a hybrid model to detect common vulnerabilities in smart contracts based on a lightweight pre-trained languagemodel BERT and connected to a bidirectional gate recurrent unitmodel.The downstream neural network adopts the bidirectional gate recurrent unit neural network model with a hierarchical attention mechanism to mine more semantic features contained in the source code of smart contracts by using their characteristics.Our experiments show that our proposed hybrid neural network model SolBERT-BiGRU-Attention is fitted by a large number of data samples with smart contract vulnerabilities,and it is found that compared with the existing methods,the accuracy of our model can reach 93.85%,and the Micro-F1 Score is 94.02%.展开更多
Personalized recommendation algorithms,which are effective means to solve information overload,are popular topics in current research.In this paper,a recommender system combining popularity and novelty(RSCPN)based on ...Personalized recommendation algorithms,which are effective means to solve information overload,are popular topics in current research.In this paper,a recommender system combining popularity and novelty(RSCPN)based on one-mode projection of weighted bipartite network is proposed.The edge between a user and item is weighted with the item’s rating,and we consider the difference in the ratings of different users for an item to obtain a reasonable method of measuring the similarity between users.RSCPN can be used in the same model for popularity and novelty recommendation by setting different parameter values and analyzing how a change in parameters affects the popularity and novelty of the recommender system.We verify and compare the accuracy,diversity and novelty of the proposed model with those of other models,and results show that RSCPN is feasible.展开更多
The rapid development of Internet of Things(IoT)technology has made previously unavailable data available,and applications can take advantage of device data for people to visualize,explore,and build complex analyses.A...The rapid development of Internet of Things(IoT)technology has made previously unavailable data available,and applications can take advantage of device data for people to visualize,explore,and build complex analyses.As the size of the network and the number of network users continue to increase,network requests tend to aggregate on a small number of network resources,which results in uneven load on network requests.Real-time,highly reliable network file distribution technology is of great importance in the Internet of Things.This paper studies real-time and highly reliable file distribution technology for large-scale networks.In response to this topic,this paper studies the current file distribution technology,proposes a file distribution model,and proposes a corresponding load balancing method based on the file distribution model.Experiments show that the system has achieved real-time and high reliability of network transmission.展开更多
In the information age,electronic documents(e-documents)have become a popular alternative to paper documents due to their lower costs,higher dissemination rates,and ease of knowledge sharing.However,digital copyright ...In the information age,electronic documents(e-documents)have become a popular alternative to paper documents due to their lower costs,higher dissemination rates,and ease of knowledge sharing.However,digital copyright infringements occur frequently due to the ease of copying,which not only infringes on the rights of creators but also weakens their creative enthusiasm.Therefore,it is crucial to establish an e-document sharing system that enforces copyright protection.However,the existing centralized system has outstanding vulnerabilities,and the plagiarism detection algorithm used cannot fully detect the context,semantics,style,and other factors of the text.Digital watermark technology is only used as a means of infringement tracing.This paper proposes a decentralized framework for e-document sharing based on decentralized autonomous organization(DAO)and non-fungible token(NFT)in blockchain.The use of blockchain as a distributed credit base resolves the vulnerabilities inherent in traditional centralized systems.The e-document evaluation and plagiarism detection mechanisms based on the DAO model effectively address challenges in comprehensive text information checks,thereby promoting the enhancement of e-document quality.The mechanism for protecting and circulating e-document copyrights using NFT technology ensures effective safeguarding of users’e-document copyrights and facilitates e-document sharing.Moreover,recognizing the security issues within the DAO governance mechanism,we introduce an innovative optimization solution.Through experimentation,we validate the enhanced security of the optimized governance mechanism,reducing manipulation risks by up to 51%.Additionally,by utilizing evolutionary game analysis to deduce the equilibrium strategies of the framework,we discovered that adjusting the reward and penalty parameters of the incentive mechanism motivates creators to generate superior quality and unique e-documents,while evaluators are more likely to engage in assessments.展开更多
As the scale of federated learning expands,solving the Non-IID data problem of federated learning has become a key challenge of interest.Most existing solutions generally aim to solve the overall performance improveme...As the scale of federated learning expands,solving the Non-IID data problem of federated learning has become a key challenge of interest.Most existing solutions generally aim to solve the overall performance improvement of all clients;however,the overall performance improvement often sacrifices the performance of certain clients,such as clients with less data.Ignoring fairness may greatly reduce the willingness of some clients to participate in federated learning.In order to solve the above problem,the authors propose Ada-FFL,an adaptive fairness federated aggregation learning algorithm,which can dynamically adjust the fairness coefficient according to the update of the local models,ensuring the convergence performance of the global model and the fairness between federated learning clients.By integrating coarse-grained and fine-grained equity solutions,the authors evaluate the deviation of local models by considering both global equity and individual equity,then the weight ratio will be dynamically allocated for each client based on the evaluated deviation value,which can ensure that the update differences of local models are fully considered in each round of training.Finally,by combining a regularisation term to limit the local model update to be closer to the global model,the sensitivity of the model to input perturbations can be reduced,and the generalisation ability of the global model can be improved.Through numerous experiments on several federal data sets,the authors show that our method has more advantages in convergence effect and fairness than the existing baselines.展开更多
With the development of Information technology and the popularization of Internet,whenever and wherever possible,people can connect to the Internet optionally.Meanwhile,the security of network traffic is threatened by...With the development of Information technology and the popularization of Internet,whenever and wherever possible,people can connect to the Internet optionally.Meanwhile,the security of network traffic is threatened by various of online malicious behaviors.The aim of an intrusion detection system(IDS)is to detect the network behaviors which are diverse and malicious.Since a conventional firewall cannot detect most of the malicious behaviors,such as malicious network traffic or computer abuse,some advanced learning methods are introduced and integrated with intrusion detection approaches in order to improve the performance of detection approaches.However,there are very few related studies focusing on both the effective detection for attacks and the representation for malicious behaviors with graph.In this paper,a novel intrusion detection approach IDBFG(Intrusion Detection Based on Feature Graph)is proposed which first filters normal connections with grid partitions,and then records the patterns of various attacks with a novel graph structure,and the behaviors in accordance with the patterns in graph are detected as intrusion behaviors.The experimental results on KDD-Cup 99 dataset show that IDBFG performs better than SVM(Supprot Vector Machines)and Decision Tree which are trained and tested in original feature space in terms of detection rates,false alarm rates and run time.展开更多
Recommender systems are very useful for people to explore what they really need.Academic papers are important achievements for researchers and they often have a great deal of choice to submit their papers.In order to ...Recommender systems are very useful for people to explore what they really need.Academic papers are important achievements for researchers and they often have a great deal of choice to submit their papers.In order to improve the efficiency of selecting the most suitable journals for publishing their works,journal recommender systems(JRS)can automatically provide a small number of candidate journals based on key information such as the title and the abstract.However,users or journal owners may attack the system for their own purposes.In this paper,we discuss about the adversarial attacks against content-based filtering JRS.We propose both targeted attack method that makes some target journals appear more often in the system and non-targeted attack method that makes the system provide incorrect recommendations.We also conduct extensive experiments to validate the proposed methods.We hope this paper could help improve JRS by realizing the existence of such adversarial attacks.展开更多
Electronic voting has partially solved the problems of poor anonymity and low efficiency associated with traditional voting.However,the difficulties it introduces into the supervision of the vote counting,as well as i...Electronic voting has partially solved the problems of poor anonymity and low efficiency associated with traditional voting.However,the difficulties it introduces into the supervision of the vote counting,as well as its need for a concurrent guaranteed trusted third party,should not be overlooked.With the advent of blockchain technology in recent years,its features such as decentralization,anonymity,and non-tampering have made it a good candidate in solving the problems that electronic voting faces.In this study,we propose a multi-candidate voting model based on the blockchain technology.With the introduction of an asymmetric encryption and an anonymity-preserving voting algorithm,votes can be counted without relying on a third party,and the voting results can be displayed in real time in a manner that satisfies various levels of voting security and privacy requirements.Experimental results show that the proposed model solves the aforementioned problems of electronic voting without significant negative impact from an increasing number of voters or candidates.展开更多
The rapid development of blockchain technology has provided new ideas for network security research.Blockchain-based network security enhancement solutions are attracting widespread attention.This paper proposes an In...The rapid development of blockchain technology has provided new ideas for network security research.Blockchain-based network security enhancement solutions are attracting widespread attention.This paper proposes an Internet domain name verification method based on blockchain.The authenticity of DNS(Domain Name System)resolution results is crucial for ensuring the accessibility of Internet services.Due to the lack of adequate security mechanisms,it has always been a challenge to verify the authenticity of Internet domain name resolution results.Although the solution represented by DNSSEC(Domain Name System Security Extensions)can theoretically solve the domain name verification problem,it has not been widely deployed on a global scale due to political,economic,and technical constraints.We argue that the root cause of this problem lies in the significant centralization of the DNS system.This centralized feature not only reduces the efficiency of domain name verification but also has the hidden risks of single point of failure and unilateral control.Internet users may disappear from the Internet due to the results of fake,subverted,or misconfigured domain name resolution.This paper presents a decentralized DNS cache verification method,which uses the consortium blockchain to replace the root domain name server to verify the authenticity of the domain name.Compared with DNSSEC’s domain name verification process,the verification efficiency of this method has increased by 30%,and there is no single point of failure or unilateral control risk.In addition,this solution is incrementally deployable,and even if it is deployed on a small number of content delivery network servers,satisfactory results can be obtained.展开更多
With the arrival of new data acquisition platforms derived from the Internet of Things(IoT),this paper goes beyond the understanding of traditional remote sensing technologies.Deep fusion of remote sensing and compute...With the arrival of new data acquisition platforms derived from the Internet of Things(IoT),this paper goes beyond the understanding of traditional remote sensing technologies.Deep fusion of remote sensing and computer vision has hit the industrial world and makes it possible to apply Artificial intelligence to solve problems such as automatic extraction of information and image interpretation.However,due to the complex architecture of IoT and the lack of a unified security protection mechanism,devices in remote sensing are vulnerable to privacy leaks when sharing data.It is necessary to design a security scheme suitable for computation‐limited devices in IoT,since traditional encryption methods are based on computational complexity.Visual Cryptography(VC)is a threshold scheme for images that can be decoded directly by the human visual system when superimposing encrypted images.The stacking‐to‐see feature and simple Boolean decryption operation make VC an ideal solution for privacy‐preserving recognition for large‐scale remote sensing images in IoT.In this study,the secure and efficient transmission of high‐resolution remote sensing images by meaningful VC is achieved.By diffusing the error between the encryption block and the original block to adjacent blocks,the degradation of quality in recovery images is mitigated.By fine‐tuning the pre‐trained model from large‐scale datasets,we improve the recognition performance of small encryption datasets for remote sensing images.The experimental results show that the proposed lightweight privacy‐preserving recognition framework maintains high recognition performance while enhancing security.展开更多
基金sponsored by the National Key R&D Program of China(No.2018YFB2100400)the National Natural Science Foundation of China(No.62002077,61872100)+4 种基金the Major Research Plan of the National Natural Science Foundation of China(92167203)the Guangdong Basic and Applied Basic Research Foundation(No.2020A1515110385)the China Postdoctoral Science Foundation(No.2022M710860)the Zhejiang Lab(No.2020NF0AB01)Guangzhou Science and Technology Plan Project(202102010440).
文摘Benefiting from the development of Federated Learning(FL)and distributed communication systems,large-scale intelligent applications become possible.Distributed devices not only provide adequate training data,but also cause privacy leakage and energy consumption.How to optimize the energy consumption in distributed communication systems,while ensuring the privacy of users and model accuracy,has become an urgent challenge.In this paper,we define the FL as a 3-layer architecture including users,agents and server.In order to find a balance among model training accuracy,privacy-preserving effect,and energy consumption,we design the training process of FL as game models.We use an extensive game tree to analyze the key elements that influence the players’decisions in the single game,and then find the incentive mechanism that meet the social norms through the repeated game.The experimental results show that the Nash equilibrium we obtained satisfies the laws of reality,and the proposed incentive mechanism can also promote users to submit high-quality data in FL.Following the multiple rounds of play,the incentive mechanism can help all players find the optimal strategies for energy,privacy,and accuracy of FL in distributed communication systems.
基金supported by the Key-Area Research and Development Program of Guangdong Province 2020B0101090003CCF-NSFOCUS Kunpeng Scientific Research Fund (CCFNSFOCUS 2021010)+4 种基金Innovation Fund Program of the Engineering Research Center for Integration and Application of Digital Learning Technology of Ministry of Education under Grant No.1221027National Natural Science Foundation of China (Grant Nos.61902083,62172115,61976064)Guangdong Higher Education Innovation Group 2020KCXTD007 and Guangzhou Higher Education Innovation Group (No.202032854)Guangzhou Fundamental Research Plan of“Municipal-School”Jointly Funded Projects (No.202102010445)Guangdong Province Science and Technology Planning Project (No.2020A1414010370).
文摘Decentralized finance(DeFi)is a general term for a series of financial products and services.It is based on blockchain technology and has attracted people’s attention because of its open,transparent,and intermediary free.Among them,the DeFi ecosystem based on Ethereum-based blockchains attracts the most attention.However,the current decentralized financial system built on the Ethereum architecture has been exposed to many smart contract vulnerabilities during the last few years.Herein,we believe it is time to improve the understanding of the prevailing Ethereum-based DeFi ecosystem security issues.To that end,we investigate the Ethereum-based DeFi security issues:1)inherited from the real-world financial system,which can be solved by macro-control;2)induced by the problems of blockchain architecture,which require a better blockchain platform;3)caused by DeFi invented applications,which should be focused on during the project development.Based on that,we further discuss the current solutions and potential directions ofDeFi security.According to our research,we could provide a comprehensive vision to the research community for the improvement of Ethereum-basedDeFi ecosystem security.
基金funded by The National Natural Science Foundation of China under Grant(No.62273108,62306081)The Youth Project of Guangdong Artificial Intelligence and Digital Economy Laboratory(Guangzhou)(PZL2022KF0006)+3 种基金The National Key Research and Development Program of China(2022YFB3604502)Special Fund Project of GuangzhouScience and Technology Innovation Development(202201011307)Guangdong Province Industrial Internet Identity Analysis and Construction Guidance Fund Secondary Node Project(1746312)Special Projects in Key Fields of General Colleges and Universities in Guangdong Province(2021ZDZX1016).
文摘Beyond-5G(B5G)aims to meet the growing demands of mobile traffic and expand the communication space.Considering that intelligent applications to B5G wireless communications will involve security issues regarding user data and operational data,this paper analyzes the maximum capacity of the multi-watermarking method for multimedia signal hiding as a means of alleviating the information security problem of B5G.The multiwatermarking process employs spread transform dither modulation.During the watermarking procedure,Gram-Schmidt orthogonalization is used to obtain the multiple spreading vectors.Consequently,multiple watermarks can be simultaneously embedded into the same position of a multimedia signal.Moreover,the multiple watermarks can be extracted without affecting one another during the extraction process.We analyze the effect of the size of the spreading vector on the unit maximum capacity,and consequently derive the theoretical relationship between the size of the spreading vector and the unit maximum capacity.A number of experiments are conducted to determine the optimal parameter values for maximum robustness on the premise of high capacity and good imperceptibility.
文摘Blockchain technology has been extensively studied over the past decade as a foundation for decentralized information-sharing platforms due to its promising potential.Despite the success of existing blockchain architectures like Bitcoin,Ethereum,Filecoin,Hyperledger Fabric,BCOS,and BCS,current blockchain applications are still quite limited.Blockchain struggles with scenarios requiring high-speed transactions(e.g.,online markets)or large data storage(e.g.,video services)due to consensus efficiency limitations.Security restrictions pose risks to investors in blockchain-based economic systems(e.g.,DeFi),deterring current and potential investors.Privacy protection challenges make it difficult to involve sensitive data in blockchain applications.
基金This work is funded by the National Natural Science Foundation of China under Grant U1636215the National key research and development plan under Grant Nos.2018YFB0803504,2016YFB0800303.
文摘Network Security Situation Awareness System YHSAS acquires,understands and displays the security factors which cause changes of network situation,and predicts the future development trend of these security factors.YHSAS is developed for national backbone network,large network operators,large enterprises and other large-scale network.This paper describes its architecture and key technologies:Network Security Oriented Total Factor Information Collection and High-Dimensional Vector Space Analysis,Knowledge Representation and Management of Super Large-Scale Network Security,Multi-Level,Multi-Granularity and Multi-Dimensional Network Security Index Construction Method,Multi-Mode and Multi-Granularity Network Security Situation Prediction Technology,and so on.The performance tests show that YHSAS has high real-time performance and accuracy in security situation analysis and trend prediction.The system meets the demands of analysis and prediction for large-scale network security situation.
基金This work is supported by the National Natural Science Foundation of China(Grants Nos.61672020,U1803263,61662069,61762068,31560622,31260538,30960246,31672385,71761029)Project funded by China Postdoctoral Science Foundation(2013M542560,2015T81129)+6 种基金A Project of Shandong Province Higher Educational Science and Technology Program(No.J16LN61)Inner Mongolia Colleges and Universities Scientific and Technological Research Projects(Grant No.NJZC17148)CERNET Innovation Project(No.NGII20161209)Natural Science Foundation of Inner Mongolia Autonomous Region of china(No.2017MS0610,No.2017MS717)Program for Young Talents of Science and Technology in Universities of Inner Mongolia Autonomous Region(No.NJYT-18-A13)Inner Mongolia Key Laboratory of economic data analysis and mining China-Mongolia Scientific Research Capacity Building of Incubator,Joint Laboratory and Technology Transfer Center,Education research project of national finance and economics(No.MZCJYB1803)Postgraduate research and innovation project of Inner Mongolia university of finance and economics.
文摘The stability of Non-Linear Feedback Shift Registers(NFSRs)plays an important role in the cryptographic security.Due to the complexity of nonlinear systems and the lack of efficient algebraic tools,the theorems related to the stability of NFSRs are still not well-developed.In this paper,we view the NFSR with periodic inputs as a Boolean control network.Based on the mathematical tool of semi-tensor product(STP),the Boolean network can be mapped into an algebraic form.Through these basic theories,we analyze the state space of non-autonomous NFSRs,and discuss the stability of an NFSR with periodic inputs of limited length or unlimited length.The simulation results are provided to prove the efficiency of the model.Based on these works,we can provide a method to analyze the stability of the NFSR with periodic input,including limited length and unlimited length.By this,we can efficiently reduce the computational complexity,and its efficiency is demonstrated by applying the theorem in simulations dealing with the stability of a non-autonomous NFSR.
基金supported by the National Natural Science Foundation of China under NO.61572153, NO. 61702220, NO. 61702223, and NO. U1636215the National Key research and Development Plan (Grant No. 2018YFB0803504)
文摘In recent years, with the rapid development of sensing technology and deployment of various Internet of Everything devices, it becomes a crucial and practical challenge to enable real-time search queries for objects, data, and services in the Internet of Everything. Moreover, such efficient query processing techniques can provide strong facilitate the research on Internet of Everything security issues. By looking into the unique characteristics in the IoE application environment, such as high heterogeneity, high dynamics, and distributed, we develop a novel search engine model, and build a dynamic prediction model of the IoE sensor time series to meet the real-time requirements for the Internet of Everything search environment. We validated the accuracy and effectiveness of the dynamic prediction model using a public sensor dataset from Intel Lab.
基金This work was supported in part by the Guangdong Province Key Research and Development Plan(Grant No.2019B010137004)the National Key research and Development Plan(Grant No.2018YFB0803504).
文摘BGP monitors are currently the main data resource of AS-level topology measurement,and the integrity of measurement result is limited to the location of such BGP monitors.However,there is currently no work to conduct a comprehensive study of the range of measurement results for a single BGP monitor.In this paper,we take the first step to describe the observed topology of each BGP monitor.To that end,we first investigate the construction and theoretical up-limit of the measured topology of a BGP monitor based on the valley-free model,then we evaluate the individual parts of the measured topology by comparing such theoretical results with the actually observed data.We find that:1)for more than 90%of the monitors,the actually observed peer-peer links merely takes a small part of all theoretical visible links;2)increasing the BGP monitors in the same AS may improve the measurement result,but with limited improvement;and 3)deploying multiple BGP monitors in different ASs can significantly improve the measurement results,but non-local BGP monitors can hardly replace the local AS BGP monitors.We also propose a metric for monitor selection optimization,and prove its effectiveness with experiment evaluation.
基金This work is funded by the National Key Research and Development Plan(Grant No.2018YFB0803504)the National Natural Science Foundation of China(Nos.61702223,61702220,61871140,U1636215)the Opening Project of Shanghai Trusted Industrial Control Platform.
文摘Programmable Logic Controllers(PLC),core of industrial control systems,is widely used in industrial control systems.The security of PLC is the key to the security of industrial control systems.Nowadays,a large number of industrial control systems are connected to the Internet which exposes the PLC equipment to the Internet,and thus raising security concerns.First of all,we introduce the basic principle of PLC in this paper.Then we analyze the PLC code security,firmware security,network security,virus vulnerability and Modbus communication protocol by reviewing the previous related work.Finally,we make a summary of the current security protection methods.
文摘This paper attempts to estimate diagnostically relevant measure,i.e.,Arteriovenous Ratio with an improved retinal vessel classification using feature ranking strategies and multiple classifiers decision-combination scheme.The features exploited for retinal vessel characterization are based on statistical measures of histogram,different filter responses of images and local gradient in-formation.The feature selection process is based on two feature ranking approaches(Pearson Correlation Coefficient technique and Relief-F method)to rank the features followed by use of maximum classification accuracy of three supervised classifiers(κ-Nearest Neighbor,Support Vector Machine and Naïve Bayes)as a threshold for feature subset selection.Retinal vessels are labeled using the selected feature subset and proposed hybrid classification scheme,i.e.,decision fusion of multiple classifiers.The comparative analysis shows an increase in vessel classification accuracy as well as Arteriovenous Ratio calculation performance.The system is tested on three databases,a local dataset of 44 images and two publically available databases,INSPIRE-AVR containing 40 images and VICAVR containing 58 images.The local database also contains images with pathologically diseased structures.The performance of the proposed system is assessed by comparing the experimental results with the gold standard estimations as well as with the results of previous methodologies.Overall,an accuracy of 90.45%,93.90%and 87.82%is achieved in retinal blood vessel separation with 0.0565,0.0650 and 0.0849 mean error in Arte-riovenous Ratio calculation for Local,INSPIRE-AVR and VICAVR dataset,respectively.
基金supported by the National Natural Science Foundation of China(Grant Nos.62272120,62106030,U20B2046,62272119,61972105)the Technology Innovation and Application Development Projects of Chongqing(Grant Nos.cstc2021jscx-gksbX0032,cstc2021jscxgksbX0029).
文摘In recent years,with the great success of pre-trained language models,the pre-trained BERT model has been gradually applied to the field of source code understanding.However,the time cost of training a language model from zero is very high,and how to transfer the pre-trained language model to the field of smart contract vulnerability detection is a hot research direction at present.In this paper,we propose a hybrid model to detect common vulnerabilities in smart contracts based on a lightweight pre-trained languagemodel BERT and connected to a bidirectional gate recurrent unitmodel.The downstream neural network adopts the bidirectional gate recurrent unit neural network model with a hierarchical attention mechanism to mine more semantic features contained in the source code of smart contracts by using their characteristics.Our experiments show that our proposed hybrid neural network model SolBERT-BiGRU-Attention is fitted by a large number of data samples with smart contract vulnerabilities,and it is found that compared with the existing methods,the accuracy of our model can reach 93.85%,and the Micro-F1 Score is 94.02%.
基金Project funded by the National Science Foundation of China under Grant(Nos.61462091,61672020,U1803263,61866039,61662085)by the Data Driven Software Engineering innovation team of Yunnan province(No.2017HC012)+2 种基金by Scientific Research Foundation Project of Yunnan Education Department(No.2019J0008,2019J0010)by China Postdoctoral Science Foundation(Nos.2013M542560,2015T81129)A Project of Shandong Province Higher Educational Science and Technology Program(No.J16LN61).
文摘Personalized recommendation algorithms,which are effective means to solve information overload,are popular topics in current research.In this paper,a recommender system combining popularity and novelty(RSCPN)based on one-mode projection of weighted bipartite network is proposed.The edge between a user and item is weighted with the item’s rating,and we consider the difference in the ratings of different users for an item to obtain a reasonable method of measuring the similarity between users.RSCPN can be used in the same model for popularity and novelty recommendation by setting different parameter values and analyzing how a change in parameters affects the popularity and novelty of the recommender system.We verify and compare the accuracy,diversity and novelty of the proposed model with those of other models,and results show that RSCPN is feasible.
基金This work was supported by National Key Research&Development Plan of China under Grant 2016QY05X1000National Natural Science Foundation of China under Grant No.61771166CERNET Innovation Project(NGII20170412).
文摘The rapid development of Internet of Things(IoT)technology has made previously unavailable data available,and applications can take advantage of device data for people to visualize,explore,and build complex analyses.As the size of the network and the number of network users continue to increase,network requests tend to aggregate on a small number of network resources,which results in uneven load on network requests.Real-time,highly reliable network file distribution technology is of great importance in the Internet of Things.This paper studies real-time and highly reliable file distribution technology for large-scale networks.In response to this topic,this paper studies the current file distribution technology,proposes a file distribution model,and proposes a corresponding load balancing method based on the file distribution model.Experiments show that the system has achieved real-time and high reliability of network transmission.
基金This work is supported by the National Key Research and Development Program(2022YFB2702300)National Natural Science Foundation of China(Grant No.62172115)+2 种基金Innovation Fund Program of the Engineering Research Center for Integration and Application of Digital Learning Technology of Ministry of Education under Grant Number No.1331005Guangdong Higher Education Innovation Group 2020KCXTD007Guangzhou Fundamental Research Plan of Municipal-School Jointly Funded Projects(No.202102010445).
文摘In the information age,electronic documents(e-documents)have become a popular alternative to paper documents due to their lower costs,higher dissemination rates,and ease of knowledge sharing.However,digital copyright infringements occur frequently due to the ease of copying,which not only infringes on the rights of creators but also weakens their creative enthusiasm.Therefore,it is crucial to establish an e-document sharing system that enforces copyright protection.However,the existing centralized system has outstanding vulnerabilities,and the plagiarism detection algorithm used cannot fully detect the context,semantics,style,and other factors of the text.Digital watermark technology is only used as a means of infringement tracing.This paper proposes a decentralized framework for e-document sharing based on decentralized autonomous organization(DAO)and non-fungible token(NFT)in blockchain.The use of blockchain as a distributed credit base resolves the vulnerabilities inherent in traditional centralized systems.The e-document evaluation and plagiarism detection mechanisms based on the DAO model effectively address challenges in comprehensive text information checks,thereby promoting the enhancement of e-document quality.The mechanism for protecting and circulating e-document copyrights using NFT technology ensures effective safeguarding of users’e-document copyrights and facilitates e-document sharing.Moreover,recognizing the security issues within the DAO governance mechanism,we introduce an innovative optimization solution.Through experimentation,we validate the enhanced security of the optimized governance mechanism,reducing manipulation risks by up to 51%.Additionally,by utilizing evolutionary game analysis to deduce the equilibrium strategies of the framework,we discovered that adjusting the reward and penalty parameters of the incentive mechanism motivates creators to generate superior quality and unique e-documents,while evaluators are more likely to engage in assessments.
基金National Natural Science Foundation of China,Grant/Award Number:62272114Joint Research Fund of Guangzhou and University,Grant/Award Number:202201020380+3 种基金Guangdong Higher Education Innovation Group,Grant/Award Number:2020KCXTD007Pearl River Scholars Funding Program of Guangdong Universities(2019)National Key R&D Program of China,Grant/Award Number:2022ZD0119602Major Key Project of PCL,Grant/Award Number:PCL2022A03。
文摘As the scale of federated learning expands,solving the Non-IID data problem of federated learning has become a key challenge of interest.Most existing solutions generally aim to solve the overall performance improvement of all clients;however,the overall performance improvement often sacrifices the performance of certain clients,such as clients with less data.Ignoring fairness may greatly reduce the willingness of some clients to participate in federated learning.In order to solve the above problem,the authors propose Ada-FFL,an adaptive fairness federated aggregation learning algorithm,which can dynamically adjust the fairness coefficient according to the update of the local models,ensuring the convergence performance of the global model and the fairness between federated learning clients.By integrating coarse-grained and fine-grained equity solutions,the authors evaluate the deviation of local models by considering both global equity and individual equity,then the weight ratio will be dynamically allocated for each client based on the evaluated deviation value,which can ensure that the update differences of local models are fully considered in each round of training.Finally,by combining a regularisation term to limit the local model update to be closer to the global model,the sensitivity of the model to input perturbations can be reduced,and the generalisation ability of the global model can be improved.Through numerous experiments on several federal data sets,the authors show that our method has more advantages in convergence effect and fairness than the existing baselines.
基金This research was funded in part by the National Natural Science Foundation of China(61871140,61872100,61572153,U1636215,61572492,61672020)the National Key research and Development Plan(Grant No.2018YFB0803504)Open Fund of Beijing Key Laboratory of IOT Information Security Technology(J6V0011104).
文摘With the development of Information technology and the popularization of Internet,whenever and wherever possible,people can connect to the Internet optionally.Meanwhile,the security of network traffic is threatened by various of online malicious behaviors.The aim of an intrusion detection system(IDS)is to detect the network behaviors which are diverse and malicious.Since a conventional firewall cannot detect most of the malicious behaviors,such as malicious network traffic or computer abuse,some advanced learning methods are introduced and integrated with intrusion detection approaches in order to improve the performance of detection approaches.However,there are very few related studies focusing on both the effective detection for attacks and the representation for malicious behaviors with graph.In this paper,a novel intrusion detection approach IDBFG(Intrusion Detection Based on Feature Graph)is proposed which first filters normal connections with grid partitions,and then records the patterns of various attacks with a novel graph structure,and the behaviors in accordance with the patterns in graph are detected as intrusion behaviors.The experimental results on KDD-Cup 99 dataset show that IDBFG performs better than SVM(Supprot Vector Machines)and Decision Tree which are trained and tested in original feature space in terms of detection rates,false alarm rates and run time.
基金This work is supported by the National Natural Science Foundation of China under Grant Nos.U1636215,61902082the Guangdong Key R&D Program of China 2019B010136003Guangdong Province Universities and Colleges Pearl River Scholar Funded Scheme(2019).
文摘Recommender systems are very useful for people to explore what they really need.Academic papers are important achievements for researchers and they often have a great deal of choice to submit their papers.In order to improve the efficiency of selecting the most suitable journals for publishing their works,journal recommender systems(JRS)can automatically provide a small number of candidate journals based on key information such as the title and the abstract.However,users or journal owners may attack the system for their own purposes.In this paper,we discuss about the adversarial attacks against content-based filtering JRS.We propose both targeted attack method that makes some target journals appear more often in the system and non-targeted attack method that makes the system provide incorrect recommendations.We also conduct extensive experiments to validate the proposed methods.We hope this paper could help improve JRS by realizing the existence of such adversarial attacks.
基金This work was supported in part by Shandong Provincial Natural Science Foundation(ZR2019PF007)the National Key Research and Development Plan of China(2018YFB0803504)+2 种基金Basic Scientific Research Operating Expenses of Shandong University(2018ZQXM004)Guangdong Province Key Research and Development Plan(2019B010137004)the National Natural Science Foundation of China(U20B2046).
文摘Electronic voting has partially solved the problems of poor anonymity and low efficiency associated with traditional voting.However,the difficulties it introduces into the supervision of the vote counting,as well as its need for a concurrent guaranteed trusted third party,should not be overlooked.With the advent of blockchain technology in recent years,its features such as decentralization,anonymity,and non-tampering have made it a good candidate in solving the problems that electronic voting faces.In this study,we propose a multi-candidate voting model based on the blockchain technology.With the introduction of an asymmetric encryption and an anonymity-preserving voting algorithm,votes can be counted without relying on a third party,and the voting results can be displayed in real time in a manner that satisfies various levels of voting security and privacy requirements.Experimental results show that the proposed model solves the aforementioned problems of electronic voting without significant negative impact from an increasing number of voters or candidates.
基金This work was supported in National Natural Science Foundation of China(Grant Nos.61976064,U20B2046)National Defence Science and Technology Key Laboratory Fund 61421190306)+1 种基金Guangdong Province Universities and Colleges Pearl River Scholar Funded Scheme(2019)National Key research and Development Plan(Grant No.2018YFB1800702).
文摘The rapid development of blockchain technology has provided new ideas for network security research.Blockchain-based network security enhancement solutions are attracting widespread attention.This paper proposes an Internet domain name verification method based on blockchain.The authenticity of DNS(Domain Name System)resolution results is crucial for ensuring the accessibility of Internet services.Due to the lack of adequate security mechanisms,it has always been a challenge to verify the authenticity of Internet domain name resolution results.Although the solution represented by DNSSEC(Domain Name System Security Extensions)can theoretically solve the domain name verification problem,it has not been widely deployed on a global scale due to political,economic,and technical constraints.We argue that the root cause of this problem lies in the significant centralization of the DNS system.This centralized feature not only reduces the efficiency of domain name verification but also has the hidden risks of single point of failure and unilateral control.Internet users may disappear from the Internet due to the results of fake,subverted,or misconfigured domain name resolution.This paper presents a decentralized DNS cache verification method,which uses the consortium blockchain to replace the root domain name server to verify the authenticity of the domain name.Compared with DNSSEC’s domain name verification process,the verification efficiency of this method has increased by 30%,and there is no single point of failure or unilateral control risk.In addition,this solution is incrementally deployable,and even if it is deployed on a small number of content delivery network servers,satisfactory results can be obtained.
基金supported in part by the National Natural Science Foundation of China under Grants(62250410365,62071084)the Guangdong Basic and Applied Basic Research Foundation of China(2022A1515011542)the Guangzhou Science and technology program of China(202201010606).
文摘With the arrival of new data acquisition platforms derived from the Internet of Things(IoT),this paper goes beyond the understanding of traditional remote sensing technologies.Deep fusion of remote sensing and computer vision has hit the industrial world and makes it possible to apply Artificial intelligence to solve problems such as automatic extraction of information and image interpretation.However,due to the complex architecture of IoT and the lack of a unified security protection mechanism,devices in remote sensing are vulnerable to privacy leaks when sharing data.It is necessary to design a security scheme suitable for computation‐limited devices in IoT,since traditional encryption methods are based on computational complexity.Visual Cryptography(VC)is a threshold scheme for images that can be decoded directly by the human visual system when superimposing encrypted images.The stacking‐to‐see feature and simple Boolean decryption operation make VC an ideal solution for privacy‐preserving recognition for large‐scale remote sensing images in IoT.In this study,the secure and efficient transmission of high‐resolution remote sensing images by meaningful VC is achieved.By diffusing the error between the encryption block and the original block to adjacent blocks,the degradation of quality in recovery images is mitigated.By fine‐tuning the pre‐trained model from large‐scale datasets,we improve the recognition performance of small encryption datasets for remote sensing images.The experimental results show that the proposed lightweight privacy‐preserving recognition framework maintains high recognition performance while enhancing security.
