Detecting Smart Contract Vulnerabilities based on Fusing Semantic and Syntax Structure Information

Abstract

Due to the widespread application and economic value of smart contracts, they have become targets for attackers, leading to significant economic losses from vulnerabilities. Therefore, it is crucial to detect potential vulnerabilities in smart contracts before they are deployed. However, existing machine learning approaches often overlook the type information of nodes and edges, while those based on heterogeneous graphs only utilize the semantic information of smart contracts, neglecting the syntax structure information. This oversight compromises the performance in detecting vulnerabilities. To address these issues, we propose a novel smart contract vulnerability detection approach named HG-Detector(Heterogeneous Graph Detector), which stands for Heterogeneous Graph Detector. This approach integrates semantic and syntax structure information by employing a heterogeneous graph neural network to analyze the source code of smart contracts. It extracts both semantic and syntax structure information and then uses a classifier to detect potential vulnerabilities. Experimental results on a dataset comprising 1269 smart contracts show that, compared to MANDO, HG-Detector has achieved an average increase of 10.06% in Precision, an average increase of 1.61% in Recall, an average increase of 2.29% in the F1, and an average increase of 4.78% in Accuracy across seven types of vulnerabilities