The rise in the adoption of blockchain technology has led to increased illegal activities by cybercriminals costing billions of dollars.Many machine learning algorithms are applied to detect such illegal behavior.Thes...The rise in the adoption of blockchain technology has led to increased illegal activities by cybercriminals costing billions of dollars.Many machine learning algorithms are applied to detect such illegal behavior.These algorithms are often trained on the transaction behavior and,in some cases,trained on the vulnerabilities that exist in the system.In our approach,we study the feasibility of using the Domain Name(DN)associated with the account in the blockchain and identify whether an account should be tagged malicious or not.Here,we leverage the temporal aspects attached to the DN.Our approach achieves 89.53%balanced-accuracy in detecting malicious blockchain DNs.While our results identify 73769 blockchain DNs that show malicious behavior at least once,out of these,34171 blockchain DNs show persistent malicious behavior,resulting in 2479 malicious blockchain DNs over time.Nonetheless,none of these identified malicious DNs were reported in new officially tagged malicious blockchain DNs.展开更多
在快速发展的信息化时代,各领域数据增长飞快,但由于数据来源多、结构松散、关系复杂多样,数据共享存在一定的困难。本体的出现促进了各领域的信息共享,以军事领域为例,构建军事事件本体、武器装备本体和战场环境本体模型。然后建立属...在快速发展的信息化时代,各领域数据增长飞快,但由于数据来源多、结构松散、关系复杂多样,数据共享存在一定的困难。本体的出现促进了各领域的信息共享,以军事领域为例,构建军事事件本体、武器装备本体和战场环境本体模型。然后建立属性关系、时空关系和语义关系来表达领域信息关联关系,综合考虑军事领域多要素,清晰地表达军事事件、武器装备、战场环境之间的复杂关系;最后,将语义网规则语言(Semantic Web Rule Language,SWRL)特点与本体相结合,对本体进行语义推理,挖掘领域内存在的隐含关系,为实现各领域的信息共享和智能检索提供基础。展开更多
Problems in unsteady aerodynamics and aeroacoustics can sometimes be formulated as integral equations,such as the boundary integral equations.Numerical discretization of integral equations in the time domain often lea...Problems in unsteady aerodynamics and aeroacoustics can sometimes be formulated as integral equations,such as the boundary integral equations.Numerical discretization of integral equations in the time domain often leads to so-called March-On-in-Time(MOT)schemes.In the literature,the temporal basis functions used in MOT schemes have been largely limited to low-order shifted Lagrange basis functions.In order to evaluate the accuracy and effectiveness of the temporal basis functions,a Fourier analysis of the temporal interpolation schemes is carried out.Based on the Fourier analysis,the spectral resolutions of various temporal basis functions are quantified.It is argued that hybrid temporal basis functions be used for interpolation of the numerical solution and its derivatives with respect to time.Stability of the proposed hybrid schemes is studied by a matrix eigenvalue method.Substantial improvement in accuracy and efficiency by using the hybrid temporal basis functions for time domain integral equations is demonstrated by numerical examples.Compared with the traditional temporal basis functions,the use of hybrid basis functions keeps numerical errors low for a larger frequency range given the same time step size.Conversely,for a given range of frequency of interest,a larger time step can be used with the hybrid temporal basis functions,resulting in an increase in computational efficiency and,at the same time,a reduction in memory requirement.展开更多
基金partially funded by the National Blockchain Project(grant number NCSC/CS/2017518)at Indian Institute of Technology KanpurIndia sponsored by the National Cyber Security Coordinator's office of the Government of India and partially by the C3i Center funding from the Science and Engineering Research Board of the Government of India(grant number SERB/CS/2016466).
文摘The rise in the adoption of blockchain technology has led to increased illegal activities by cybercriminals costing billions of dollars.Many machine learning algorithms are applied to detect such illegal behavior.These algorithms are often trained on the transaction behavior and,in some cases,trained on the vulnerabilities that exist in the system.In our approach,we study the feasibility of using the Domain Name(DN)associated with the account in the blockchain and identify whether an account should be tagged malicious or not.Here,we leverage the temporal aspects attached to the DN.Our approach achieves 89.53%balanced-accuracy in detecting malicious blockchain DNs.While our results identify 73769 blockchain DNs that show malicious behavior at least once,out of these,34171 blockchain DNs show persistent malicious behavior,resulting in 2479 malicious blockchain DNs over time.Nonetheless,none of these identified malicious DNs were reported in new officially tagged malicious blockchain DNs.
文摘在快速发展的信息化时代,各领域数据增长飞快,但由于数据来源多、结构松散、关系复杂多样,数据共享存在一定的困难。本体的出现促进了各领域的信息共享,以军事领域为例,构建军事事件本体、武器装备本体和战场环境本体模型。然后建立属性关系、时空关系和语义关系来表达领域信息关联关系,综合考虑军事领域多要素,清晰地表达军事事件、武器装备、战场环境之间的复杂关系;最后,将语义网规则语言(Semantic Web Rule Language,SWRL)特点与本体相结合,对本体进行语义推理,挖掘领域内存在的隐含关系,为实现各领域的信息共享和智能检索提供基础。
基金This work was supported in part by a NASA Cooperative Agreement,NNX11AI63A.
文摘Problems in unsteady aerodynamics and aeroacoustics can sometimes be formulated as integral equations,such as the boundary integral equations.Numerical discretization of integral equations in the time domain often leads to so-called March-On-in-Time(MOT)schemes.In the literature,the temporal basis functions used in MOT schemes have been largely limited to low-order shifted Lagrange basis functions.In order to evaluate the accuracy and effectiveness of the temporal basis functions,a Fourier analysis of the temporal interpolation schemes is carried out.Based on the Fourier analysis,the spectral resolutions of various temporal basis functions are quantified.It is argued that hybrid temporal basis functions be used for interpolation of the numerical solution and its derivatives with respect to time.Stability of the proposed hybrid schemes is studied by a matrix eigenvalue method.Substantial improvement in accuracy and efficiency by using the hybrid temporal basis functions for time domain integral equations is demonstrated by numerical examples.Compared with the traditional temporal basis functions,the use of hybrid basis functions keeps numerical errors low for a larger frequency range given the same time step size.Conversely,for a given range of frequency of interest,a larger time step can be used with the hybrid temporal basis functions,resulting in an increase in computational efficiency and,at the same time,a reduction in memory requirement.
