In recent years,the number of smart contracts deployed on blockchain has exploded.However,the issue of vulnerability has caused incalculable losses.Due to the irreversible and immutability of smart contracts,vulnerabi...In recent years,the number of smart contracts deployed on blockchain has exploded.However,the issue of vulnerability has caused incalculable losses.Due to the irreversible and immutability of smart contracts,vulnerability detection has become particularly important.With the popular use of neural network model,there has been a growing utilization of deep learning-based methods and tools for the identification of vulnerabilities within smart contracts.This paper commences by providing a succinct overview of prevalent categories of vulnerabilities found in smart contracts.Subsequently,it categorizes and presents an overview of contemporary deep learning-based tools developed for smart contract detection.These tools are categorized based on their open-source status,the data format and the type of feature extraction they employ.Then we conduct a comprehensive comparative analysis of these tools,selecting representative tools for experimental validation and comparing them with traditional tools in terms of detection coverage and accuracy.Finally,Based on the insights gained from the experimental results and the current state of research in the field of smart contract vulnerability detection tools,we suppose to provide a reference standard for developers of contract vulnerability detection tools.Meanwhile,forward-looking research directions are also proposed for deep learning-based smart contract vulnerability detection.展开更多
With the rise of blockchain technology,the security issues of smart contracts have become increasingly critical.Despite the availability of numerous smart contract vulnerability detection tools,many face challenges su...With the rise of blockchain technology,the security issues of smart contracts have become increasingly critical.Despite the availability of numerous smart contract vulnerability detection tools,many face challenges such as slow updates,usability issues,and limited installation methods.These challenges hinder the adoption and practicality of these tools.This paper examines smart contract vulnerability detection tools from 2016 to 2023,sourced from the Web of Science(WOS)and Google Scholar.By systematically collecting,screening,and synthesizing relevant research,38 open-source tools that provide installation methods were selected for further investigation.From a developer’s perspective,this paper offers a comprehensive survey of these 38 open-source tools,discussing their operating principles,installation methods,environmental dependencies,update frequencies,and installation challenges.Based on this,we propose an Ethereum smart contract vulnerability detection framework.This framework enables developers to easily utilize various detection tools and accurately analyze contract security issues.To validate the framework’s stability,over 1700 h of testing were conducted.Additionally,a comprehensive performance test was performed on the mainstream detection tools integrated within the framework,assessing their hardware requirements and vulnerability detection coverage.Experimental results indicate that the Slither tool demonstrates satisfactory performance in terms of system resource consumption and vulnerability detection coverage.This study represents the first performance evaluation of testing tools in this domain,providing significant reference value.展开更多
Hierarchical dendritic micro–nano structure Zn Fe_2O_4 have been prepared by electrochemical reduction and thermal oxidation method in this work. X-ray diffractometry, Raman spectra and field-emission scanning electr...Hierarchical dendritic micro–nano structure Zn Fe_2O_4 have been prepared by electrochemical reduction and thermal oxidation method in this work. X-ray diffractometry, Raman spectra and field-emission scanning electron microscopy were used to characterize the crystal structure, size and morphology. The results show that the sample(S-2) is composed of pure ZnFe_2O_4 when the molar ratio of Zn^(2+)/Fe^(2+)in the electrolyte is 0.35. Decreasing the molar ratio of Zn^(2+)/Fe^(2+), the sample(S-1) is composed of ZnFe_2O_4 and α-Fe_2O_3, whereas increasing the molar ratio of Zn^(2+)/Fe^(2+), the sample(S-3) is composed of ZnFe_2O_4 and Zn O. The lattice parameters of ZnFe_2O_4 are influenced by the molar ratio of Zn^(2+)/Fe: Zn at excess decreases the cell volume whereas Fe at excess increases the cell volume of Zn Fe_2O_4. All the samples have the dendritic structure, of which S-2 has micron-sized lush branches with nano-sized leaves. UV–Vis diffuse reflectance spectra were acquired by a spectrophotometer. The absorption edges gradually blue shift with the increase of the molar ratio of Zn^(2+)/Fe^(2+). Photocatalytic activities for water splitting were investigated under Xe light irradiation in an aqueous olution containing 0.1 mol·L^(-1)Na_2S/0.02 mol·L^(-1)Na_2SO_3 in a glass reactor. The relatively highest photocatalytic activity with 1.41 μmol·h-1· 0.02 g^(-1)was achieved by pure ZnFe_2O_4sample(S-2). The photocatalytic activity of the mixture phase of Zn Fe_2O_4 and α-Fe_2O_3(S-1) is better than ZnF e_2O_4 and ZnO(S-3).展开更多
基金funded by the Major PublicWelfare Special Fund of Henan Province(No.201300210200)the Major Science and Technology Research Special Fund of Henan Province(No.221100210400).
文摘In recent years,the number of smart contracts deployed on blockchain has exploded.However,the issue of vulnerability has caused incalculable losses.Due to the irreversible and immutability of smart contracts,vulnerability detection has become particularly important.With the popular use of neural network model,there has been a growing utilization of deep learning-based methods and tools for the identification of vulnerabilities within smart contracts.This paper commences by providing a succinct overview of prevalent categories of vulnerabilities found in smart contracts.Subsequently,it categorizes and presents an overview of contemporary deep learning-based tools developed for smart contract detection.These tools are categorized based on their open-source status,the data format and the type of feature extraction they employ.Then we conduct a comprehensive comparative analysis of these tools,selecting representative tools for experimental validation and comparing them with traditional tools in terms of detection coverage and accuracy.Finally,Based on the insights gained from the experimental results and the current state of research in the field of smart contract vulnerability detection tools,we suppose to provide a reference standard for developers of contract vulnerability detection tools.Meanwhile,forward-looking research directions are also proposed for deep learning-based smart contract vulnerability detection.
基金supported by the Major Public Welfare Special Fund of Henan Province(No.201300210200)the Major Science and Technology Research Special Fund of Henan Province(No.221100210400).
文摘With the rise of blockchain technology,the security issues of smart contracts have become increasingly critical.Despite the availability of numerous smart contract vulnerability detection tools,many face challenges such as slow updates,usability issues,and limited installation methods.These challenges hinder the adoption and practicality of these tools.This paper examines smart contract vulnerability detection tools from 2016 to 2023,sourced from the Web of Science(WOS)and Google Scholar.By systematically collecting,screening,and synthesizing relevant research,38 open-source tools that provide installation methods were selected for further investigation.From a developer’s perspective,this paper offers a comprehensive survey of these 38 open-source tools,discussing their operating principles,installation methods,environmental dependencies,update frequencies,and installation challenges.Based on this,we propose an Ethereum smart contract vulnerability detection framework.This framework enables developers to easily utilize various detection tools and accurately analyze contract security issues.To validate the framework’s stability,over 1700 h of testing were conducted.Additionally,a comprehensive performance test was performed on the mainstream detection tools integrated within the framework,assessing their hardware requirements and vulnerability detection coverage.Experimental results indicate that the Slither tool demonstrates satisfactory performance in terms of system resource consumption and vulnerability detection coverage.This study represents the first performance evaluation of testing tools in this domain,providing significant reference value.
基金Supported by the State Key Laboratory of Urban Water Resource and Environment,Harbin Institute of Technology(2015DX07)
文摘Hierarchical dendritic micro–nano structure Zn Fe_2O_4 have been prepared by electrochemical reduction and thermal oxidation method in this work. X-ray diffractometry, Raman spectra and field-emission scanning electron microscopy were used to characterize the crystal structure, size and morphology. The results show that the sample(S-2) is composed of pure ZnFe_2O_4 when the molar ratio of Zn^(2+)/Fe^(2+)in the electrolyte is 0.35. Decreasing the molar ratio of Zn^(2+)/Fe^(2+), the sample(S-1) is composed of ZnFe_2O_4 and α-Fe_2O_3, whereas increasing the molar ratio of Zn^(2+)/Fe^(2+), the sample(S-3) is composed of ZnFe_2O_4 and Zn O. The lattice parameters of ZnFe_2O_4 are influenced by the molar ratio of Zn^(2+)/Fe: Zn at excess decreases the cell volume whereas Fe at excess increases the cell volume of Zn Fe_2O_4. All the samples have the dendritic structure, of which S-2 has micron-sized lush branches with nano-sized leaves. UV–Vis diffuse reflectance spectra were acquired by a spectrophotometer. The absorption edges gradually blue shift with the increase of the molar ratio of Zn^(2+)/Fe^(2+). Photocatalytic activities for water splitting were investigated under Xe light irradiation in an aqueous olution containing 0.1 mol·L^(-1)Na_2S/0.02 mol·L^(-1)Na_2SO_3 in a glass reactor. The relatively highest photocatalytic activity with 1.41 μmol·h-1· 0.02 g^(-1)was achieved by pure ZnFe_2O_4sample(S-2). The photocatalytic activity of the mixture phase of Zn Fe_2O_4 and α-Fe_2O_3(S-1) is better than ZnF e_2O_4 and ZnO(S-3).
