An Efficient Code-Embedding-Based Vulnerability Detection Model for Ethereum Smart Contracts

Zhigang Xu, Xingxing Chen, Xinhua Dong, Hongmu Han, Zhongzhen Yan, Kangze Ye, Chaojun Li, Zhiqiang Zheng, Haitao Wang and Jiaxi Zhang
Additional contact information
Zhigang Xu: Hubei University of Technology, China
Xingxing Chen: Hubei University of Technology, China
Xinhua Dong: Hubei University of Technology, China
Hongmu Han: Hubei University of Technology, China
Zhongzhen Yan: Hubei University of Technology, China
Kangze Ye: Hubei University of Technology, China
Chaojun Li: Hubei University of Technology, China
Zhiqiang Zheng: Narcotics Control Bureau of Department of Public Security of Guangdong Province, China
Haitao Wang: Narcotics Control Bureau of Department of Public Security of Guangdong Province, China
Jiaxi Zhang: Narcotics Control Bureau of Department of Public Security of Guangdong Province, China

International Journal of Data Warehousing and Mining (IJDWM), 2023, vol. 19, issue 2, 1-23

Abstract: Efficient and convenient vulnerability detection for smart contracts is a key issue in the field of smart contracts. The earlier vulnerability detection for smart contracts mainly relies on static symbol analysis, which has high accuracy but low efficiency and is prone to path explosion. In this paper, the authors propose a static method for vulnerability detection based on deep learning. It first disassembles Ethereum smart contracts into opcode sequences and then converts the vulnerability detection problem into a natural language text classification problem. The word vector method is employed to map each opcode to a uniform vector space, and the opcode sequence matrix is trained by the TextCNN method to detect vulnerabilities. Furthermore, a code obfuscation method is given to enhance and balance the dataset, while three different opcode sequence generation methods are proposed to construct features. The experimental results verify that the average prediction accuracy of each smart contract exceeds 96%, and the average detection time is less than 0.1 s.

Date: 2023
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/IJDWM.320473 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:igg:jdwm00:v:19:y:2023:i:2:p:1-23

Access Statistics for this article

International Journal of Data Warehousing and Mining (IJDWM) is currently edited by Eric Pardede

More articles in International Journal of Data Warehousing and Mining (IJDWM) from IGI Global
Bibliographic data for series maintained by Journal Editor ().