Research on Smart Contract Vulnerability Detection Based on Graph Neural Networks

Abstract

With the widespread application of blockchain technology, the security issues of smart contracts have become increasingly prominent. Traditional contract vulnerability detection methods mostly depend on source code, making it hard to adapt to the actual situation where only bytecode is available on-chain. This paper presents a novel bytecode-based vulnerability detection method for smart contracts (BC-GNN). By parsing the contract bytecode, it extracts opcode sequences, control flow graphs (CFGs), and data flow graphs (DFGs), integrates them into a unified heterogeneous graph structure, and uses graph neural networks (GNNs) to automatically detect multiple types of vulnerabilities. Experimental results show that BC-GNN achieves excellent performance on multiple actual and enhanced datasets. In particular, it demonstrates strong applicability and generalization ability in scenarios where source code is unavailable. This approach offers new ideas and technical pathways for blockchain smart contract security detection.