A Heterogeneous Graph and Multi-Feature Fusion Based Framework for Smart Contract Vulnerability Detection

Abstract

In recent years, graph neural networks have demonstrated strong capabilities in processing graph-structured data and have made significant progress in the field of smart contract vulnerability detection. This paper introduces HF-Sec, a novel framework for smart contract vulnerability detection. The framework first automatically generates heterogeneous contract graphs from the source code of Ethereum smart contracts to represent the control flow and function call relationships of the code. Then, by using a multi-source attention mechanism, the framework is able to synthesize features from different sources to capture key information from multiple perspectives. In addition, HF-Sec utilizes Fast Graph Transformer Networks and Kolmogorov-Arnold Networks to automatically extract mission-critical meta-paths and enhance the interpretability of the model. We performed experimental validation on a mixed dataset containing 423 contracts with vulnerabilities and 2742 contracts without vulnerabilities. The experimental results show that HF-Sec can significantly improve the accuracy of smart contract vulnerability detection, which is better than the methods based on machine learning or traditional analysis techniques. Through a series of ablation experiments, we further verified the importance of various key components in HF-Sec to improve the detection accuracy.