Traditional Chinese Medicine(TCM)is one of the most promising programs for disease prevention and treatment.Meanwhile,the quality of TCM has garnered much attention.To ensure the quality of TCM,many works are based on...Traditional Chinese Medicine(TCM)is one of the most promising programs for disease prevention and treatment.Meanwhile,the quality of TCM has garnered much attention.To ensure the quality of TCM,many works are based on the blockchain scheme to design the traceability scheme of TCM to trace its origin.Although these schemes can ensure the integrity,sharability,credibility,and immutability of TCM more effectively,many problems are exposed with the rapid growth of TCM data in blockchains,such as expensive overhead,performance bottlenecks,and the traditional blockchain architecture is unsuitable for TCM data with dynamic growth.Motivated by the aforementioned problems,we propose a novel and lightweight TCM traceability architecture based on the blockchain using sharding(LBS-TCM).Compared to the existing blockchain-based TCM traceability system,our architecture utilizes sharding to develop a novel traceability mechanism that supports more convenient traceability operations for TCM requirements such as uploading,querying,and downloading.Specifically,our architecture consists of a leader shard blockchain layer as its main component,which employs a sharding mechanism to conveniently TCM tracing.Empirical evaluations demonstrated that our architecture showed better performance in many aspects compared to traditional blockchain architectures,such as TCM transaction processing,TCM transaction querying,TCM uploading,etc.In our architecture,tracing TCM has become a very efficient operation,which ensures the quality of TCM and provides great convenience for subsequent TCM analysis and retrospective research.展开更多
An obviously challenging problem in named entity recognition is the construction of the kind data set of entities.Although some research has been conducted on entity database construction,the majority of them are dire...An obviously challenging problem in named entity recognition is the construction of the kind data set of entities.Although some research has been conducted on entity database construction,the majority of them are directed at Wikipedia or the minority at structured entities such as people,locations and organizational nouns in the news.This paper focuses on the identification of scientific entities in carbonate platforms in English literature,using the example of carbonate platforms in sedimentology.Firstly,based on the fact that the reasons for writing literature in key disciplines are likely to be provided by multidisciplinary experts,this paper designs a literature content extraction method that allows dealing with complex text structures.Secondly,based on the literature extraction content,we formalize the entity extraction task(lexicon and lexical-based entity extraction)for entity extraction.Furthermore,for testing the accuracy of entity extraction,three currently popular recognition methods are chosen to perform entity detection in this paper.Experiments show that the entity data set provided by the lexicon and lexical-based entity extraction method is of significant assistance for the named entity recognition task.This study presents a pilot study of entity extraction,which involves the use of a complex structure and specialized literature on carbonate platforms in English.展开更多
The blockchain provides a reliable and scalable method for enabling source-tracing functionality in large-scale Internet of Things(IoT)systems.Traditional blockchain-based source tracing applications are generally bas...The blockchain provides a reliable and scalable method for enabling source-tracing functionality in large-scale Internet of Things(IoT)systems.Traditional blockchain-based source tracing applications are generally based on the hypothesis that the raw data collected by each IoT node are credible and consistent,which however may not always be the truth.As no mechanism ensures the reliability of the original data collected from the IoT devices,these data may be accidently screwed up or maliciously tampered with before they are uploaded on-chain.To address this issue,we propose the Multi-dimensional Certificates of Origin(MCO)method to filter out the potentially incredible data-till all the data uploaded to the chain are credible.To achieve this,we devise the Multidimensional Information Cross-Verification(MICV)and Multi-source Data Matching Calculation(MDMC)methods.MICV verifies whether a to-be-uploaded datum is consistent or credible,and MDMC determines which data should be discarded and which data should be kept to retain the most likely credible/untampered ones in the circumstance when data inconsistency appears.Large-scale experiments show that our scheme ensures on the credibility of data and off the chain with an affordable overhead.展开更多
Secret image sharing(SIS)is a significant research topic of image information hiding,which divides the image into multiple shares and dis-tributes them to multiple parties for management and preservation.In order to r...Secret image sharing(SIS)is a significant research topic of image information hiding,which divides the image into multiple shares and dis-tributes them to multiple parties for management and preservation.In order to reconstruct the original image,a subset with predetermined number of shares is needed.And just because it is not necessary to use all of the shares to make a reconstruction,SIS creates a high fault tolerance which breaks the limitations of traditional image protection methods,but at the same time,it causes a reduce of safety.Recently,new technologies,such as deep learning and blockchain,have been applied into SIS to improve its efficiency and security.This paper gives an overall review of SIS,discusses four important approaches for SIS,and makes a comparison analysis among them from the perspectives of pixel expansion,tamper resistance,etc.At the end,this paper indicates the possible research directions of SIS in the future.展开更多
In recent years,deep learning has been the mainstream technology for fingerprint liveness detection(FLD)tasks because of its remarkable performance.However,recent studies have shown that these deep fake fingerprint de...In recent years,deep learning has been the mainstream technology for fingerprint liveness detection(FLD)tasks because of its remarkable performance.However,recent studies have shown that these deep fake fingerprint detection(DFFD)models are not resistant to attacks by adversarial examples,which are generated by the introduction of subtle perturbations in the fingerprint image,allowing the model to make fake judgments.Most of the existing adversarial example generation methods are based on gradient optimization,which is easy to fall into local optimal,resulting in poor transferability of adversarial attacks.In addition,the perturbation added to the blank area of the fingerprint image is easily perceived by the human eye,leading to poor visual quality.In response to the above challenges,this paper proposes a novel adversarial attack method based on local adaptive gradient variance for DFFD.The ridge texture area within the fingerprint image has been identified and designated as the region for perturbation generation.Subsequently,the images are fed into the targeted white-box model,and the gradient direction is optimized to compute gradient variance.Additionally,an adaptive parameter search method is proposed using stochastic gradient ascent to explore the parameter values during adversarial example generation,aiming to maximize adversarial attack performance.Experimental results on two publicly available fingerprint datasets show that ourmethod achieves higher attack transferability and robustness than existing methods,and the perturbation is harder to perceive.展开更多
Software-Defined Networking(SDN)is an emerging architecture that enables a computer network to be intelligently and centrally controlled via software applications.It can help manage the whole network environment in a ...Software-Defined Networking(SDN)is an emerging architecture that enables a computer network to be intelligently and centrally controlled via software applications.It can help manage the whole network environment in a consistent and holistic way,without the need of understanding the underlying network structure.At present,SDN may face many challenges like insider attacks,i.e.,the centralized control plane would be attacked by malicious underlying devices and switches.To protect the security of SDN,effective detection approaches are indispensable.In the literature,challenge-based collaborative intrusion detection networks(CIDNs)are an effective detection framework in identifying malicious nodes.It calculates the nodes'reputation and detects a malicious node by sending out a special message called a challenge.In this work,we devise a challenge-based CIDN in SDN and measure its performance against malicious internal nodes.Our results demonstrate that such a mechanism can be effective in SDN environments.展开更多
A Large-Scale Heterogeneous Network(LS-HetNet)integrates different networks into one uniform network system to provide seamless one-world network coverage.In LS-HetNet,various devices use different technologies to acc...A Large-Scale Heterogeneous Network(LS-HetNet)integrates different networks into one uniform network system to provide seamless one-world network coverage.In LS-HetNet,various devices use different technologies to access heterogeneous networks and generate a large amount of data.For dealing with a large number of access requirements,these data are usually stored in the HetNet Domain Management Server(HDMS)of the current domain,and HDMS uses a centralized Authentication/Authorization/Auditing(AAA)scheme to protect the data.However,this centralized method easily causes the data to be modified or disclosed.To address this issue,we propose a blockchain-empowered AAA scheme for accessing data of LS-HetNet.Firstly,the account address of the blockchain is used as the identity authentication,and the access control permission of data is redesigned and stored on the blockchain,then processes of AAA are redefined.Finally,the experimental model on Ethereum private chain is built,and the results show that the scheme is not only secure but also decentral,without tampering and trustworthiness.展开更多
Reweighting adversarial examples during training plays an essential role in improving the robustness of neural networks,which lies in the fact that examples closer to the decision boundaries are much more vulnerable t...Reweighting adversarial examples during training plays an essential role in improving the robustness of neural networks,which lies in the fact that examples closer to the decision boundaries are much more vulnerable to being attacked and should be given larger weights.The probability margin(PM)method is a promising approach to continuously and path-independently mea-suring such closeness between the example and decision boundary.However,the performance of PM is limited due to the fact that PM fails to effectively distinguish the examples having only one misclassified category and the ones with multiple misclassified categories,where the latter is closer to multi-classification decision boundaries and is supported to be more critical in our observation.To tackle this problem,this paper proposed an improved PM criterion,called confused-label-based PM(CL-PM),to measure the closeness mentioned above and reweight adversarial examples during training.Specifi-cally,a confused label(CL)is defined as the label whose prediction probability is greater than that of the ground truth label given a specific adversarial example.Instead of considering the discrepancy between the probability of the true label and the probability of the most misclassified label as the PM method does,we evaluate the closeness by accumulating the probability differences of all the CLs and ground truth label.CL-PM shares a negative correlation with data vulnerability:data with larger/smaller CL-PM is safer/riskier and should have a smaller/larger weight.Experiments demonstrated that CL-PM is more reliable in indicating the closeness regarding multiple misclassified categories,and reweighting adversarial training based on CL-PM outperformed state-of-the-art counterparts.展开更多
In recent years,many adversarial malware examples with different feature strategies,especially GAN and its variants,have been introduced to handle the security threats,e.g.,evading the detection of machine learning de...In recent years,many adversarial malware examples with different feature strategies,especially GAN and its variants,have been introduced to handle the security threats,e.g.,evading the detection of machine learning detectors.However,these solutions still suffer from problems of complicated deployment or long running time.In this paper,we propose an n-gram MalGAN method to solve these problems.We borrow the idea of n-gram from the Natural Language Processing(NLP)area to expand feature sources for adversarial malware examples in MalGAN.Generally,the n-gram MalGAN obtains the feature vector directly from the hexadecimal bytecodes of the executable file.It can be implemented easily and conveniently with a simple program language(e.g.,C++),with no need for any prior knowledge of the executable file or any professional feature extraction tools.These features are functionally independent and thus can be added to the non-functional area of the malicious program to maintain its original executability.In this way,the n-gram could make the adversarial attack easier and more convenient.Experimental results show that the evasion rate of the n-gram MalGAN is at least 88.58%to attack different machine learning algorithms under an appropriate group rate,growing to even 100%for the Random Forest algorithm.展开更多
基金supported by the research and innovation program for graduate students of the Guangzhou University of Traditional Chinese MedicineThis work is also partially supported by the National Key Research and Development Program of China(2019YFC1710402)the research on tracing TCM Electronic Medical Records Based on the Lightweight Blockchain of Guangdong Provincial Bureau of Traditional Chinese Medicine(20222045).
文摘Traditional Chinese Medicine(TCM)is one of the most promising programs for disease prevention and treatment.Meanwhile,the quality of TCM has garnered much attention.To ensure the quality of TCM,many works are based on the blockchain scheme to design the traceability scheme of TCM to trace its origin.Although these schemes can ensure the integrity,sharability,credibility,and immutability of TCM more effectively,many problems are exposed with the rapid growth of TCM data in blockchains,such as expensive overhead,performance bottlenecks,and the traditional blockchain architecture is unsuitable for TCM data with dynamic growth.Motivated by the aforementioned problems,we propose a novel and lightweight TCM traceability architecture based on the blockchain using sharding(LBS-TCM).Compared to the existing blockchain-based TCM traceability system,our architecture utilizes sharding to develop a novel traceability mechanism that supports more convenient traceability operations for TCM requirements such as uploading,querying,and downloading.Specifically,our architecture consists of a leader shard blockchain layer as its main component,which employs a sharding mechanism to conveniently TCM tracing.Empirical evaluations demonstrated that our architecture showed better performance in many aspects compared to traditional blockchain architectures,such as TCM transaction processing,TCM transaction querying,TCM uploading,etc.In our architecture,tracing TCM has become a very efficient operation,which ensures the quality of TCM and provides great convenience for subsequent TCM analysis and retrospective research.
基金supported by the National Natural Science Foundation of China under Grant No.42050102the National Science Foundation of China(Grant No.62001236)the Natural Science Foundation of the Jiangsu Higher Education Institutions of China(Grant No.20KJA520003).
文摘An obviously challenging problem in named entity recognition is the construction of the kind data set of entities.Although some research has been conducted on entity database construction,the majority of them are directed at Wikipedia or the minority at structured entities such as people,locations and organizational nouns in the news.This paper focuses on the identification of scientific entities in carbonate platforms in English literature,using the example of carbonate platforms in sedimentology.Firstly,based on the fact that the reasons for writing literature in key disciplines are likely to be provided by multidisciplinary experts,this paper designs a literature content extraction method that allows dealing with complex text structures.Secondly,based on the literature extraction content,we formalize the entity extraction task(lexicon and lexical-based entity extraction)for entity extraction.Furthermore,for testing the accuracy of entity extraction,three currently popular recognition methods are chosen to perform entity detection in this paper.Experiments show that the entity data set provided by the lexicon and lexical-based entity extraction method is of significant assistance for the named entity recognition task.This study presents a pilot study of entity extraction,which involves the use of a complex structure and specialized literature on carbonate platforms in English.
基金This study is supported by Foundation of National Natural Science Foundation of China(Grant Number:62072273,72111530206,61962009,61873117,61832012,61771231,61771289)Natural Science Foundation of Shandong Province(ZR2019MF062)+3 种基金Shandong University Science and Technology Program Project(J18A326)Guangxi Key Laboratory of Cryptography and Information Security(No:GCIS202112)The Major Basic Research Project of Natural Science Foundation of Shandong Province of China(ZR2018ZC0438)Major Scientific and Technological Special Project of Guizhou Province(20183001),Foundation of Guizhou Provincial Key Laboratory of Public Big Data(No.2019BDKFJJ009),Talent project of Guizhou Big Data Academy.Guizhou Provincial Key Laboratory of Public Big Data.([2018]01).
文摘The blockchain provides a reliable and scalable method for enabling source-tracing functionality in large-scale Internet of Things(IoT)systems.Traditional blockchain-based source tracing applications are generally based on the hypothesis that the raw data collected by each IoT node are credible and consistent,which however may not always be the truth.As no mechanism ensures the reliability of the original data collected from the IoT devices,these data may be accidently screwed up or maliciously tampered with before they are uploaded on-chain.To address this issue,we propose the Multi-dimensional Certificates of Origin(MCO)method to filter out the potentially incredible data-till all the data uploaded to the chain are credible.To achieve this,we devise the Multidimensional Information Cross-Verification(MICV)and Multi-source Data Matching Calculation(MDMC)methods.MICV verifies whether a to-be-uploaded datum is consistent or credible,and MDMC determines which data should be discarded and which data should be kept to retain the most likely credible/untampered ones in the circumstance when data inconsistency appears.Large-scale experiments show that our scheme ensures on the credibility of data and off the chain with an affordable overhead.
基金supported in part by the National Natural Science Foundation of China under Grant 61972205,Grant U1936218in part by the Guangdong Natural Science Funds for Distinguished Young Scholar+2 种基金in part by CSSC Systems Engineering Research Institute(Grant No.193-A11-107-01-33)in part by Science and Technology Planning Project of Changsha(No.kq2004004)in part by the Collaborative Innovation Center of Atmospheric Environment and Equipment Technology(CICAEET)fund,China.
文摘Secret image sharing(SIS)is a significant research topic of image information hiding,which divides the image into multiple shares and dis-tributes them to multiple parties for management and preservation.In order to reconstruct the original image,a subset with predetermined number of shares is needed.And just because it is not necessary to use all of the shares to make a reconstruction,SIS creates a high fault tolerance which breaks the limitations of traditional image protection methods,but at the same time,it causes a reduce of safety.Recently,new technologies,such as deep learning and blockchain,have been applied into SIS to improve its efficiency and security.This paper gives an overall review of SIS,discusses four important approaches for SIS,and makes a comparison analysis among them from the perspectives of pixel expansion,tamper resistance,etc.At the end,this paper indicates the possible research directions of SIS in the future.
基金supported by the National Natural Science Foundation of China under Grant(62102189,62122032,61972205)the National Social Sciences Foundation of China under Grant 2022-SKJJ-C-082+2 种基金the Natural Science Foundation of Jiangsu Province under Grant BK20200807NUDT Scientific Research Program under Grant(JS21-4,ZK21-43)Guangdong Natural Science Funds for Distinguished Young Scholar under Grant 2023B1515020041.
文摘In recent years,deep learning has been the mainstream technology for fingerprint liveness detection(FLD)tasks because of its remarkable performance.However,recent studies have shown that these deep fake fingerprint detection(DFFD)models are not resistant to attacks by adversarial examples,which are generated by the introduction of subtle perturbations in the fingerprint image,allowing the model to make fake judgments.Most of the existing adversarial example generation methods are based on gradient optimization,which is easy to fall into local optimal,resulting in poor transferability of adversarial attacks.In addition,the perturbation added to the blank area of the fingerprint image is easily perceived by the human eye,leading to poor visual quality.In response to the above challenges,this paper proposes a novel adversarial attack method based on local adaptive gradient variance for DFFD.The ridge texture area within the fingerprint image has been identified and designated as the region for perturbation generation.Subsequently,the images are fed into the targeted white-box model,and the gradient direction is optimized to compute gradient variance.Additionally,an adaptive parameter search method is proposed using stochastic gradient ascent to explore the parameter values during adversarial example generation,aiming to maximize adversarial attack performance.Experimental results on two publicly available fingerprint datasets show that ourmethod achieves higher attack transferability and robustness than existing methods,and the perturbation is harder to perceive.
基金This work was supported by National Natural Science Foundation of China(No.61802080 and 61802077)Guangdong General Colleges and Universities Research Project(2018GkQNCX105)+1 种基金Zhongshan Public Welfare Science and Technology Research Project(2019B2044)Keping Yu was supported in part by the Japan Society for the Promotion of Science(JSPS)Grants-in-Aid for Scientific Research(KAKENHI)under Grant JP18K18044.
文摘Software-Defined Networking(SDN)is an emerging architecture that enables a computer network to be intelligently and centrally controlled via software applications.It can help manage the whole network environment in a consistent and holistic way,without the need of understanding the underlying network structure.At present,SDN may face many challenges like insider attacks,i.e.,the centralized control plane would be attacked by malicious underlying devices and switches.To protect the security of SDN,effective detection approaches are indispensable.In the literature,challenge-based collaborative intrusion detection networks(CIDNs)are an effective detection framework in identifying malicious nodes.It calculates the nodes'reputation and detects a malicious node by sending out a special message called a challenge.In this work,we devise a challenge-based CIDN in SDN and measure its performance against malicious internal nodes.Our results demonstrate that such a mechanism can be effective in SDN environments.
基金This work was supported by National Natural Science Foundation of China(China)under grants 61373162Sichuan Science and Technology Support Project(China)under grants 2019YFG0183+1 种基金Visual Computing and Virtual Reality Sichuan Provincial Key Laboratory Project(China)under grants KJ201402was supported in part by the Japan Society for the Promotion of Science(JSPS)Grants-in-Aid for Scientific Research(KAKENHI)(Japan)under Grant JP18K18044.
文摘A Large-Scale Heterogeneous Network(LS-HetNet)integrates different networks into one uniform network system to provide seamless one-world network coverage.In LS-HetNet,various devices use different technologies to access heterogeneous networks and generate a large amount of data.For dealing with a large number of access requirements,these data are usually stored in the HetNet Domain Management Server(HDMS)of the current domain,and HDMS uses a centralized Authentication/Authorization/Auditing(AAA)scheme to protect the data.However,this centralized method easily causes the data to be modified or disclosed.To address this issue,we propose a blockchain-empowered AAA scheme for accessing data of LS-HetNet.Firstly,the account address of the blockchain is used as the identity authentication,and the access control permission of data is redesigned and stored on the blockchain,then processes of AAA are redefined.Finally,the experimental model on Ethereum private chain is built,and the results show that the scheme is not only secure but also decentral,without tampering and trustworthiness.
基金supported by the National Natural Science Foundation of China (No.62072127,No.62002076,No.61906049)Natural Science Foundation of Guangdong Province (No.2023A1515011774,No.2020A1515010423)+3 种基金Project 6142111180404 supported by CNKLSTISS,Science and Technology Program of Guangzhou,China (No.202002030131)Guangdong basic and applied basic research fund joint fund Youth Fund (No.2019A1515110213)Open Fund Project of Fujian Provincial Key Laboratory of Information Processing and Intelligent Control (Minjiang University) (No.MJUKF-IPIC202101)Scientific research project for Guangzhou University (No.RP2022003).
文摘Reweighting adversarial examples during training plays an essential role in improving the robustness of neural networks,which lies in the fact that examples closer to the decision boundaries are much more vulnerable to being attacked and should be given larger weights.The probability margin(PM)method is a promising approach to continuously and path-independently mea-suring such closeness between the example and decision boundary.However,the performance of PM is limited due to the fact that PM fails to effectively distinguish the examples having only one misclassified category and the ones with multiple misclassified categories,where the latter is closer to multi-classification decision boundaries and is supported to be more critical in our observation.To tackle this problem,this paper proposed an improved PM criterion,called confused-label-based PM(CL-PM),to measure the closeness mentioned above and reweight adversarial examples during training.Specifi-cally,a confused label(CL)is defined as the label whose prediction probability is greater than that of the ground truth label given a specific adversarial example.Instead of considering the discrepancy between the probability of the true label and the probability of the most misclassified label as the PM method does,we evaluate the closeness by accumulating the probability differences of all the CLs and ground truth label.CL-PM shares a negative correlation with data vulnerability:data with larger/smaller CL-PM is safer/riskier and should have a smaller/larger weight.Experiments demonstrated that CL-PM is more reliable in indicating the closeness regarding multiple misclassified categories,and reweighting adversarial training based on CL-PM outperformed state-of-the-art counterparts.
基金supported in part by National Natural Science Foundation of China(No.61802383)Research Project of Pazhou Lab for Excellent Young Scholars(No.PZL2021KF0024)+3 种基金Guangzhou Science and Technology Project Basic Research Plan(No.202201010330,202201020162)Guangdong Philosophy and Social Science Planning Project(No.GD19YYJ02)Research on the Supporting Technologies of the Metaverse in Cultural Media(No.PT252022039)National Undergraduate Training Platform for Innovation and Entrepreneurship(No.202111078029).
文摘In recent years,many adversarial malware examples with different feature strategies,especially GAN and its variants,have been introduced to handle the security threats,e.g.,evading the detection of machine learning detectors.However,these solutions still suffer from problems of complicated deployment or long running time.In this paper,we propose an n-gram MalGAN method to solve these problems.We borrow the idea of n-gram from the Natural Language Processing(NLP)area to expand feature sources for adversarial malware examples in MalGAN.Generally,the n-gram MalGAN obtains the feature vector directly from the hexadecimal bytecodes of the executable file.It can be implemented easily and conveniently with a simple program language(e.g.,C++),with no need for any prior knowledge of the executable file or any professional feature extraction tools.These features are functionally independent and thus can be added to the non-functional area of the malicious program to maintain its original executability.In this way,the n-gram could make the adversarial attack easier and more convenient.Experimental results show that the evasion rate of the n-gram MalGAN is at least 88.58%to attack different machine learning algorithms under an appropriate group rate,growing to even 100%for the Random Forest algorithm.
