Table of Content

Open Access iconOpen Access

ARTICLE

crossmark

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: email

Journal of Information Hiding and Privacy Protection 2020, 2(1), 35-45. https://doi.org/10.32604/jihpp.2020.010331

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

APA Style
Wei, Z., Wang, J., Shen, X., Luo, Q. (2020). Smart contract fuzzing based on taint analysis and genetic algorithms. Journal of Information Hiding and Privacy Protection, 2(1), 35-45. https://doi.org/10.32604/jihpp.2020.010331
Vancouver Style
Wei Z, Wang J, Shen X, Luo Q. Smart contract fuzzing based on taint analysis and genetic algorithms. J Inf Hiding Privacy Protection . 2020;2(1):35-45 https://doi.org/10.32604/jihpp.2020.010331
IEEE Style
Z. Wei, J. Wang, X. Shen, and Q. Luo "Smart Contract Fuzzing Based on Taint Analysis and Genetic Algorithms," J. Inf. Hiding Privacy Protection , vol. 2, no. 1, pp. 35-45. 2020. https://doi.org/10.32604/jihpp.2020.010331



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
  • 1764

    View

  • 1284

    Download

  • 0

    Like

Share Link