Open Access
ARTICLE
Smart Contract Fuzzing Based on Taint Analysis and Genetic Algorithms
Zaoyu Wei1, *, Jiaqi Wang2, Xueqi Shen1, Qun Luo1
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.
* Corresponding Author: Zaoyu Wei. Email: .
Journal of Quantum Computing 2020, 2(1), 11-24. https://doi.org/10.32604/jqc.2020.010815
Received 07 January 2020; Accepted 09 March 2020; Issue published 28 May 2020
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.
Keywords
Cite This Article
Z. Wei, J. Wang, X. Shen and Q. Luo, "Smart contract fuzzing based on taint analysis and genetic algorithms,"
Journal of Quantum Computing, vol. 2, no.1, pp. 11–24, 2020. https://doi.org/10.32604/jqc.2020.010815