Smart Contract Security Detection Based on Graph Neural Network

Abstract

As the core component of blockchain applications, smart contracts are increasingly scrutinized for their security. Among various vulnerabilities, infinite loop flaws pose significant threats due to their hidden nature and potential for exhausting system resources. This paper proposes a static detection method based on Graph Convolutional Networks (GCNs), which transforms smart contracts into control flow and data flow graphs. Through graph-based modeling and vectorized encoding, semantic features such as loop structures and function dependencies are effectively captured. An improved GCN architecture is employed to identify potential infinite execution patterns through neighborhood aggregation and graph-level representation learning. Experimental results demonstrate that the proposed method achieves high accuracy and F1-score across real-world contract datasets, offering an effective and scalable solution for smart contract vulnerability analysis.