SNOW: An Effective Smart Contract Reentrancy Vulnerability Detection Method Based on Joint Feature Graph and Hybrid Graph Neural Network

Abstract

ABSTRACT Background With the popularization and application of blockchain technology, smart contracts, as one of the underlying important technologies, have naturally attracted the attention of all parties. The vulnerabilities in smart contracts will lead to information leakage, asset theft, and other problems. Motivation Existing smart contract vulnerability detection tools mostly detect vulnerabilities through a set expert mode, relying more on professional knowledge. Traditional smart contract vulnerability detection methods based on deep learning rarely pay attention to syntactic information and semantic information at the same time, and their accuracy is low. Although the method based on graph neural network alleviates this problem to some extent, it suffers from the problem of too many nodes. Methods In this paper, we propose SNOW, an advanced method for detecting smart contract vulnerabilities, which leverages statement‐level joint feature graph and hybrid graph neural network to enhance the performance and efficacy of identifying smart contract vulnerabilities. Our proposed method consists of three parts. First, we generate a new graph representation called the Joint Feature Graph (JFG), which more effectively captures code information. Next, we introduce a hybrid graph neural network designed to extract JFG graph vectors more efficiently. Finally, we classify the graph vectors. Results We have conducted extensive experiments on two datasets and compared various existing methods. The results show that our method is superior to the current state‐of‐art method in many indexes such as accuracy and precision.