Combining Heterogeneous Graph Attention Networks With Expert Knowledge for Smart Contract Vulnerability Detection

Abstract

Security vulnerabilities in smart contracts pose serious threats to the trustworthiness and stability of blockchain systems. To overcome the limitations of existing detection methods in structural modeling and semantic feature representation, this paper proposes a smart contract vulnerability detection method (HEVD) that integrates heterogeneous graph attention networks with expert knowledge. HEVD achieves dynamic synergy between structural features and semantic priors through a multi-head self-attention mechanism. The heterogeneous graph attention network captures deep structural dependencies in smart contract code, while a hierarchical expert knowledge system distills high-confidence vulnerability patterns to provide explicit semantic guidance. This design effectively addresses the semantic gap of models that rely solely on data-driven training and compensates for the lack of global structural awareness in traditional expert-based approaches. Experimental results show that HEVD attains F1scores of 91.03%, 90.48%, and 83.46% for reentrancy, timestamp dependency, and infinite loop vulnerabilities, respectively, significantly outperforming multiple existing methods. Ablation studies further confirm the effectiveness of the proposed framework, demonstrating that the deep fusion of structural representation and semantic priors is key to enhancing smart contract vulnerability detection.