Recently,the Erebus attack has proved to be a security threat to the blockchain network layer,and the existing research has faced challenges in detecting the Erebus attack on the blockchain network layer.The cloud-bas...Recently,the Erebus attack has proved to be a security threat to the blockchain network layer,and the existing research has faced challenges in detecting the Erebus attack on the blockchain network layer.The cloud-based active defense and one-sidedness detection strategies are the hindrances in detecting Erebus attacks.This study designs a detection approach by establishing a ReliefF_WMRmR-based two-stage feature selection algorithm and a deep learning-based multimodal classification detection model for Erebus attacks and responding to security threats to the blockchain network layer.The goal is to improve the performance of Erebus attack detection methods,by combining the traffic behavior with the routing status based on multimodal deep feature learning.The traffic behavior and routing status were first defined and used to describe the attack characteristics at diverse stages of s leak monitoring,hidden traffic overlay,and transaction identity forgery.The goal is to clarify how an Erebus attack affects the routing transfer and traffic state on the blockchain network layer.Consequently,detecting objects is expected to become more relevant and sensitive.A two-stage feature selection algorithm was designed based on ReliefF and weighted maximum relevance minimum redundancy(ReliefF_WMRmR)to alleviate the overfitting of the training model caused by redundant information and noise in multiple source features of the routing status and traffic behavior.The ReliefF algorithm was introduced to select strong correlations and highly informative features of the labeled data.According to WMRmR,a feature selection framework was defined to eliminate weakly correlated features,eliminate redundant information,and reduce the detection overhead of the model.A multimodal deep learning model was constructed based on the multilayer perceptron(MLP)to settle the high false alarm rates incurred by multisource data.Using this model,isolated inputs and deep learning were conducted on the selected routing status and traffic behavior.Redundant intermodal information was removed because of the complementarity of the multimodal network,which was followed by feature fusion and output feature representation to boost classification detection precision.The experimental results demonstrate that the proposed method can detect features,such as traffic data,at key link nodes and route messages in a real blockchain network environment.Additionally,the model can detect Erebus attacks effectively.This study provides novelty to the existing Erebus attack detection by increasing the accuracy detection by 1.05%,the recall rate by 2.01%,and the F1-score by 2.43%.展开更多
The forking problem plays a key role in the security issue,which is a major concern in the blockchain system.Although many works studied the attack strategy,consensus mechanism,privacy-protecting and security performa...The forking problem plays a key role in the security issue,which is a major concern in the blockchain system.Although many works studied the attack strategy,consensus mechanism,privacy-protecting and security performance analysis,most of them only address the intentional forking caused by a malicious attacker.In fact,without any attacker,unintentional forking still remains due to transmission delay and failure,especially in wireless network scenarios.To this end,this paper investigates the reason for generating unintentional forking and derives the forking probability expression in Wireless Blockchain Networks(WBN).Furthermore,in order to illustrate the unintentional forking on the blockchain system,the performances in terms of resource utilization rate,block generation time,and Transaction Per Second(TPS)are investigated.The numerical results show that the target difficulty of hash algorithm in generating a new block,the delay time of broadcasting,the network scale,and the transmission failure probability would affect the unintentional forking probability significantly,which can provide a reliable basis for avoiding forking to save resource consumption and improving system performance.展开更多
Data sharing and privacy securing present extensive opportunities and challenges in vehicular network.This paper introducestrust access authentication scheme’as a mechanism to achieve real-time monitoring and promote...Data sharing and privacy securing present extensive opportunities and challenges in vehicular network.This paper introducestrust access authentication scheme’as a mechanism to achieve real-time monitoring and promote collaborative sharing for vehicles.Blockchain,which can provide secure authentication and protected privacy,is a crucial technology.However,traditional cloud computing performs poorly in supplying low-latency and fast-response services for moving vehicles.In this situation,edge computing enabled Blockchain network appeals to be a promising method,where moving vehicles can access storage or computing resource and get authenticated from Blockchain edge nodes directly.In this paper,a hierarchical architecture is proposed consist of vehicular network layer,Blockchain edge layer and Blockchain network layer.Through a authentication mechanism adopting digital signature algorithm,it achieves trusted authentication and ensures valid verification.Moreover,a caching scheme based on many-to-many matching is proposed to minimize average delivery delay of vehicles.Simulation results prove that the proposed caching scheme has a better performance than existing schemes based on central-ized model or edge caching strategy in terms of hit ratio and average delay.展开更多
Software defined optical networking(SDON)is a critical technology for the next generation network with the advantages of programmable control and etc.As one of the key issues of SDON,the security of control plane has ...Software defined optical networking(SDON)is a critical technology for the next generation network with the advantages of programmable control and etc.As one of the key issues of SDON,the security of control plane has also received extensive attention,especially in certain network scenarios with high security requirement.Due to the existence of vulnerabilities and heavy overhead,the existing firewalls and distributed control technologies cannot solve the control plane security problem well.In this paper,we propose a distributed control architecture for SDON using the blockchain technique(BlockCtrl).The proposed BlockCtrl model introduces the advantages of blockchain into SDON to achieve a high-efficiency fault tolerant control.We have evaluated the performance of our proposed architecture and compared it to the existing models with respect to various metrics including processing rate,recovery latency and etc.The numerical results show that the BlockCtrl is capable of attacks detection and fault tolerant control in SDON with high performance on resource utilization and service correlation.展开更多
Blockchain is a technology that uses community validation to keep synchronized the content of ledgers replicated across multiple users,which is the underlying technology of digital currency like bitcoin.The anonymity ...Blockchain is a technology that uses community validation to keep synchronized the content of ledgers replicated across multiple users,which is the underlying technology of digital currency like bitcoin.The anonymity of blockchain has caused widespread concern.In this paper,we put forward AABN,an Anonymity Assessment model based on Bayesian Network.Firstly,we investigate and analyze the anonymity assessment techniques,and focus on typical anonymity assessment schemes.Then the related concepts involved in the assessment model are introduced and the model construction process is described in detail.Finally,the anonymity in the MIX anonymous network is quantitatively evaluated using the methods of accurate reasoning and approximate reasoning respectively,and the anonymity assessment experiments under different output strategies of the MIX anonymous network are analyzed.展开更多
基金funded by Open Fund Project of Information Assurance Technology Key Laboratory(No.KJ-15-109)Zhengzhou Science and Technology Talents(131PLKRC644).
文摘Recently,the Erebus attack has proved to be a security threat to the blockchain network layer,and the existing research has faced challenges in detecting the Erebus attack on the blockchain network layer.The cloud-based active defense and one-sidedness detection strategies are the hindrances in detecting Erebus attacks.This study designs a detection approach by establishing a ReliefF_WMRmR-based two-stage feature selection algorithm and a deep learning-based multimodal classification detection model for Erebus attacks and responding to security threats to the blockchain network layer.The goal is to improve the performance of Erebus attack detection methods,by combining the traffic behavior with the routing status based on multimodal deep feature learning.The traffic behavior and routing status were first defined and used to describe the attack characteristics at diverse stages of s leak monitoring,hidden traffic overlay,and transaction identity forgery.The goal is to clarify how an Erebus attack affects the routing transfer and traffic state on the blockchain network layer.Consequently,detecting objects is expected to become more relevant and sensitive.A two-stage feature selection algorithm was designed based on ReliefF and weighted maximum relevance minimum redundancy(ReliefF_WMRmR)to alleviate the overfitting of the training model caused by redundant information and noise in multiple source features of the routing status and traffic behavior.The ReliefF algorithm was introduced to select strong correlations and highly informative features of the labeled data.According to WMRmR,a feature selection framework was defined to eliminate weakly correlated features,eliminate redundant information,and reduce the detection overhead of the model.A multimodal deep learning model was constructed based on the multilayer perceptron(MLP)to settle the high false alarm rates incurred by multisource data.Using this model,isolated inputs and deep learning were conducted on the selected routing status and traffic behavior.Redundant intermodal information was removed because of the complementarity of the multimodal network,which was followed by feature fusion and output feature representation to boost classification detection precision.The experimental results demonstrate that the proposed method can detect features,such as traffic data,at key link nodes and route messages in a real blockchain network environment.Additionally,the model can detect Erebus attacks effectively.This study provides novelty to the existing Erebus attack detection by increasing the accuracy detection by 1.05%,the recall rate by 2.01%,and the F1-score by 2.43%.
基金This work was supported in part by the National Natural Science Foundation of China under Grant 61701059,Grant 61941114,and Grant 61831002in part by the Fundamental Research Funds for the Central Universities of New Teachers Project,in part by the Basic and Advanced Research Projects of CSTC(No.cstc2019jcyj-zdxmX0008)+2 种基金in part by the Chongqing Science and Technology Innovation Leading Talent Support Program(CSTCCXLJR-C201710,and CSTCCXLJRC201908)in part by Chongqing Technological Innovation and Application Development Projects(cstc2019jscx-msxm1322)in part by the Science and Technology Research Program of Chongqing Municipal Education Commission(Grant No.KJZD-K201900605).
文摘The forking problem plays a key role in the security issue,which is a major concern in the blockchain system.Although many works studied the attack strategy,consensus mechanism,privacy-protecting and security performance analysis,most of them only address the intentional forking caused by a malicious attacker.In fact,without any attacker,unintentional forking still remains due to transmission delay and failure,especially in wireless network scenarios.To this end,this paper investigates the reason for generating unintentional forking and derives the forking probability expression in Wireless Blockchain Networks(WBN).Furthermore,in order to illustrate the unintentional forking on the blockchain system,the performances in terms of resource utilization rate,block generation time,and Transaction Per Second(TPS)are investigated.The numerical results show that the target difficulty of hash algorithm in generating a new block,the delay time of broadcasting,the network scale,and the transmission failure probability would affect the unintentional forking probability significantly,which can provide a reliable basis for avoiding forking to save resource consumption and improving system performance.
基金support by Research on Key Technologies of Dynamically Secure Identity Authentication and Risk Control of Power Business in the Science and Technology Project of State Grid Electric Power Company(No.5204XA19003F)National Natural Science Foundation of China(Grant No.601702048)
文摘Data sharing and privacy securing present extensive opportunities and challenges in vehicular network.This paper introducestrust access authentication scheme’as a mechanism to achieve real-time monitoring and promote collaborative sharing for vehicles.Blockchain,which can provide secure authentication and protected privacy,is a crucial technology.However,traditional cloud computing performs poorly in supplying low-latency and fast-response services for moving vehicles.In this situation,edge computing enabled Blockchain network appeals to be a promising method,where moving vehicles can access storage or computing resource and get authenticated from Blockchain edge nodes directly.In this paper,a hierarchical architecture is proposed consist of vehicular network layer,Blockchain edge layer and Blockchain network layer.Through a authentication mechanism adopting digital signature algorithm,it achieves trusted authentication and ensures valid verification.Moreover,a caching scheme based on many-to-many matching is proposed to minimize average delivery delay of vehicles.Simulation results prove that the proposed caching scheme has a better performance than existing schemes based on central-ized model or edge caching strategy in terms of hit ratio and average delay.
基金supported in part by NSFC project(61871056)Young Elite Scientists Sponsorship Program by CAST(2018QNRC001)+1 种基金Fundamental Research Funds for the Central Universities(2018XKJC06)Open Fund of SKL of IPOC(BUPT)(IPOC2018A001)
文摘Software defined optical networking(SDON)is a critical technology for the next generation network with the advantages of programmable control and etc.As one of the key issues of SDON,the security of control plane has also received extensive attention,especially in certain network scenarios with high security requirement.Due to the existence of vulnerabilities and heavy overhead,the existing firewalls and distributed control technologies cannot solve the control plane security problem well.In this paper,we propose a distributed control architecture for SDON using the blockchain technique(BlockCtrl).The proposed BlockCtrl model introduces the advantages of blockchain into SDON to achieve a high-efficiency fault tolerant control.We have evaluated the performance of our proposed architecture and compared it to the existing models with respect to various metrics including processing rate,recovery latency and etc.The numerical results show that the BlockCtrl is capable of attacks detection and fault tolerant control in SDON with high performance on resource utilization and service correlation.
基金supported by the following grants:the National Natural Science Foundation of China under Grant No.61170273the China Scholarship Council under Grant No.[2013]3050+1 种基金CCF-Tencent Open Fund WeBank Special Fuding(CCF-WebankRAGR20180104)the Beijing Natural Science Foundation(4194086)
文摘Blockchain is a technology that uses community validation to keep synchronized the content of ledgers replicated across multiple users,which is the underlying technology of digital currency like bitcoin.The anonymity of blockchain has caused widespread concern.In this paper,we put forward AABN,an Anonymity Assessment model based on Bayesian Network.Firstly,we investigate and analyze the anonymity assessment techniques,and focus on typical anonymity assessment schemes.Then the related concepts involved in the assessment model are introduced and the model construction process is described in detail.Finally,the anonymity in the MIX anonymous network is quantitatively evaluated using the methods of accurate reasoning and approximate reasoning respectively,and the anonymity assessment experiments under different output strategies of the MIX anonymous network are analyzed.
