@Article{jihpp.2020.010331,
AUTHOR = {Zaoyu Wei, Jiaqi Wang, Xueqi Shen, Qun Luo},
TITLE = {Smart Contract Fuzzing Based on Taint Analysis and Genetic Algorithms},
JOURNAL = {Journal of Information Hiding and Privacy Protection},
VOLUME = {2},
YEAR = {2020},
NUMBER = {1},
PAGES = {35--45},
URL = {http://www.techscience.com/jihpp/v2n1/40334},
ISSN = {2637-4226},
ABSTRACT = {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.},
DOI = {10.32604/jihpp.2020.010331}
}