Smart Contract Fuzzing Based on Taint Analysis and Genetic Algorithms

查看全文 作　　者：Zaoyu [1]Wei;Jiaqi [2]Wang;Xueqi [1]Shen;Qun [1]Luo

机构地区：[1]School of Cyberspace Security,Beijing University of Posts and Telecommunications,Beijing,100876,China;[2]College of New Media,Beijing Institute of Graphic Communication,Beijing,102600,China

出　　处：《Journal of Quantum Computing》索引2020年第2卷第1期,共14页

基　　金：This work is supported by the National Key R&D Program of China(2017YFB0802703);Major Scientific and Technological Special Project of Guizhou Province(20183001);Open Foundation of Guizhou Provincial Key VOLUME XX,2019 Laboratory of Public Big Data(2018BDKFJJ014);Open Foundation of Guizhou Provincial Key Laboratory of Public Big Data(2018BDKFJJ019);Open Foundation of Guizhou Provincial Key Laboratory of Public Big Data(2018BDKFJJ022). 摘　　要：Smart contract has greatly improved the services and capabilities of blockchain,but it has become the weakest link of blockchain security because of its code nature.Therefore,efficient vulnerability detection of smart contract is the key to ensure the security of blockchain system.Oriented to Ethereum smart contract,the study solves the problems of redundant input and low coverage in the smart contract fuzz.In this paper,a taint analysis method based on EVM is proposed to reduce the invalid input,a dangerous operation database is designed to identify the dangerous input,and genetic algorithm is used to optimize the code coverage of the input,which construct the fuzzing framework for smart contract together.Finally,by comparing Oyente and ContractFuzzer,the performance and efficiency of the framework are proved. 关键词：Smart contract FUZZING taint analysis genetic algorithms

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Smart Contract Fuzzing Based on Taint Analysis and Genetic Algorithms

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