Open Access

ARTICLE

Smart Contract Fuzzing Based on Taint Analysis and Genetic Algorithms

Zaoyu Wei^{1, *}, 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.

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Keywords

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