Smart Contract Vulnerability Detection Model Based on Siamese Network (SCVSN): A Case Study of Reentrancy Vulnerability

Ran Guo, Weijie Chen, Lejun Zhang, Guopeng Wang and Huiling Chen
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Ran Guo: School of Physics and Materials Science, Guangzhou University, Guangzhou 510006, China
Weijie Chen: College of Information Engineering, Yangzhou University, Yangzhou 225127, China
Lejun Zhang: College of Information Engineering, Yangzhou University, Yangzhou 225127, China
Guopeng Wang: Engineering Research Center of Integration and Application of Digital Learning Technology, Ministry of Education, Beijing 100039, China
Huiling Chen: Department of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou 325035, China

Energies, 2022, vol. 15, issue 24, 1-20

Abstract: Blockchain technology is currently evolving rapidly, and smart contracts are the hallmark of the second generation of blockchains. Currently, smart contracts are gradually being used in power system networks to build a decentralized energy system. Security is very important to power systems and attacks launched against smart contract vulnerabilities occur frequently, seriously affecting the development of the smart contract ecosystem. Current smart contract vulnerability detection tools suffer from low correct rates and high false positive rates, which cannot meet current needs. Therefore, we propose a smart contract vulnerability detection system based on the Siamese network in this paper. We improved the original Siamese network model to perform smart contract vulnerability detection by comparing the similarity of two sub networks with the same structure and shared parameters. We also demonstrate, through extensive experiments, that the model has better vulnerability detection performance and lower false alarm rate compared with previous research results.

Keywords: smart contract; deep learning; siamese network (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
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