随着区块链技术在各行各业的广泛应用,区块链系统的架构变得越来越复杂,这也增加了安全问题的数量.目前,在区块链系统中采用了模糊测试、符号执行等传统的漏洞检测方法,但这些技术无法有效检测出未知的漏洞.为了提高区块链系统的安全性...随着区块链技术在各行各业的广泛应用,区块链系统的架构变得越来越复杂,这也增加了安全问题的数量.目前,在区块链系统中采用了模糊测试、符号执行等传统的漏洞检测方法,但这些技术无法有效检测出未知的漏洞.为了提高区块链系统的安全性,提出基于形式化方法的区块链系统漏洞检测模型VDMBS(vulnerability detection model for blockchain systems),所提模型综合系统迁移状态、安全规约和节点间信任关系等多种安全因素,同时提供基于业务流程执行语言BPEL(business process execution language)的漏洞模型构建方法.最后,用NuSMV在基于区块链的电子投票选举系统上验证所提出的漏洞检测模型的有效性,实验结果表明,与现有的5种形式化测试工具相比,所提出的VDMBS模型能够检测出更多的区块链系统业务逻辑漏洞和智能合约漏洞.展开更多
Along with the rapid development of social networks, social network worms have constituted one of the major internet security problems. The root of worm is the inevitable software vulnerability during the design and i...Along with the rapid development of social networks, social network worms have constituted one of the major internet security problems. The root of worm is the inevitable software vulnerability during the design and implementation process of software. So it is hard to completely avoid worms in the existing software engineering systems. Due to lots of bandwidth consumption, the patch cannot be transmitted simultaneously by the network administrator to all hosts. This paper studies how to prevent the propagation of social network worms through the immunization of key nodes. Unlike existing containment models for worm propagation, a novel immunization strategy is proposed based on network vertex influence. The strategy selects the critical vertices in the whole network. Then the immunization is applied on the selected vertices to achieve the maximal effect of worm containment with minimal cost. Different algorithms are implemented to select vertices. Simulation experiments are presented to analyze and evaluate the performance of different algorithms.展开更多
Using degree distribution to assess network vulnerability represents a promising direction of network analysis.However,the traditional degree distribution model is inadequate for analyzing the vulnerability of spatial...Using degree distribution to assess network vulnerability represents a promising direction of network analysis.However,the traditional degree distribution model is inadequate for analyzing the vulnerability of spatial networks because it does not take into consideration the geographical aspects of spatial networks.This paper proposes a spatially weighted degree model in which both the functional class and the length of network links are considered to be important factors for determining the node degrees of spatial networks.A weight coefficient is used in this new model to account for the contribution of each factor to the node degree.The proposed model is compared with the traditional degree model and an accessibility-based vulnerability model in the vulnerabil-ity analysis of a highway network.Experiment results indicate that,although node degrees of spatial networks derived from the tra-ditional degree model follow a random distribution,node degrees determined by the spatially weighted model exhibit a scale-free distribution,which is a common characteristic of robust networks.Compared to the accessibility-based model,the proposed model has similar performance in identifying critical nodes but with higher computational efficiency and better ability to reveal the overall vulnerability of a spatial network.展开更多
文摘随着区块链技术在各行各业的广泛应用,区块链系统的架构变得越来越复杂,这也增加了安全问题的数量.目前,在区块链系统中采用了模糊测试、符号执行等传统的漏洞检测方法,但这些技术无法有效检测出未知的漏洞.为了提高区块链系统的安全性,提出基于形式化方法的区块链系统漏洞检测模型VDMBS(vulnerability detection model for blockchain systems),所提模型综合系统迁移状态、安全规约和节点间信任关系等多种安全因素,同时提供基于业务流程执行语言BPEL(business process execution language)的漏洞模型构建方法.最后,用NuSMV在基于区块链的电子投票选举系统上验证所提出的漏洞检测模型的有效性,实验结果表明,与现有的5种形式化测试工具相比,所提出的VDMBS模型能够检测出更多的区块链系统业务逻辑漏洞和智能合约漏洞.
基金supported by Fundamental Research Funds of the Central Universities under Grant no. N120317001 and N100704001Program for New Century Excellent Talents in University (NCET13-0113)+1 种基金Natural Science Foundation of Liaoning Province of China under Grant no. 201202059Program for Liaoning Excellent Talents in University under LR2013011
文摘Along with the rapid development of social networks, social network worms have constituted one of the major internet security problems. The root of worm is the inevitable software vulnerability during the design and implementation process of software. So it is hard to completely avoid worms in the existing software engineering systems. Due to lots of bandwidth consumption, the patch cannot be transmitted simultaneously by the network administrator to all hosts. This paper studies how to prevent the propagation of social network worms through the immunization of key nodes. Unlike existing containment models for worm propagation, a novel immunization strategy is proposed based on network vertex influence. The strategy selects the critical vertices in the whole network. Then the immunization is applied on the selected vertices to achieve the maximal effect of worm containment with minimal cost. Different algorithms are implemented to select vertices. Simulation experiments are presented to analyze and evaluate the performance of different algorithms.
基金Supported by the Institute of Crustal Dynamics Funds (No. ZDJ2009‐01, No. ZDJ2007‐13)
文摘Using degree distribution to assess network vulnerability represents a promising direction of network analysis.However,the traditional degree distribution model is inadequate for analyzing the vulnerability of spatial networks because it does not take into consideration the geographical aspects of spatial networks.This paper proposes a spatially weighted degree model in which both the functional class and the length of network links are considered to be important factors for determining the node degrees of spatial networks.A weight coefficient is used in this new model to account for the contribution of each factor to the node degree.The proposed model is compared with the traditional degree model and an accessibility-based vulnerability model in the vulnerabil-ity analysis of a highway network.Experiment results indicate that,although node degrees of spatial networks derived from the tra-ditional degree model follow a random distribution,node degrees determined by the spatially weighted model exhibit a scale-free distribution,which is a common characteristic of robust networks.Compared to the accessibility-based model,the proposed model has similar performance in identifying critical nodes but with higher computational efficiency and better ability to reveal the overall vulnerability of a spatial network.
