SVChecker: a deep learning-based system for smart contract vulnerability detection

Ye Yuan; TongYi Xie

doi:10.1117/12.2637775

24 May 2022 SVChecker: a deep learning-based system for smart contract vulnerability detection

Ye Yuan, TongYi Xie

Author Affiliations +

Proceedings Volume 12260, International Conference on Computer Application and Information Security (ICCAIS 2021); 122600W (2022) https://doi.org/10.1117/12.2637775
Event: International Conference on Computer Application and Information Security (ICCAIS 2021), 2021, Wuhan, China

Abstract

The detection of smart contracts vulnerability is a valuable research problem because smart contracts hold a huge amount of cryptocurrency. In the past, popular detection tools were mainly based on some traditional techniques such as fuzzing and symbolic execution, which rely on fixed expert features or patterns and often miss many vulnerabilities. Recent machine learning approaches alleviate this issue but do not notice the semantic information in the source code. In this paper, we develop a system called SVChecker to classify the smart contract source code written in Solidity. To show the superiority of our system, we conduct experiments on more than 40,000 smart contracts collected from Ethereum. Empirically, our experimental results demonstrate that our system outperforms all popular detection tools.

Citation Download Citation

Ye Yuan and TongYi Xie "SVChecker: a deep learning-based system for smart contract vulnerability detection", Proc. SPIE 12260, International Conference on Computer Application and Information Security (ICCAIS 2021), 122600W (24 May 2022); https://doi.org/10.1117/12.2637775

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KEYWORDS

Data modeling

Neural networks

Sensors

Feature extraction

Machine learning

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Scientific research

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