TCRNet: Topology-Guided Contextual Representation Network for Smart Contract Vulnerability Detection

Abstract

Smart contracts operate in decentralized environments where deployed code cannot be easily modified, making security vulnerabilities particularly critical. Even minor logical flaws may lead to severe financial and operational consequences. Although traditional static analysis and symbolic execution techniques have been widely used for vulnerability detection, they often rely on handcrafted rules and struggle to generalize to complex or evolving contract patterns. Recent deep learning approaches have improved detection performance by learning representations directly from code, yet many of these models treat source code primarily as token sequences or graph structures in isolation, limiting their ability to capture deeper semantic interactions. We propose Topology-Guided Contextual Representation Network (TCRNet), a Contextual Encoding Network framework for smart contract vulnerability detection. The model integrates multi-granular semantic information and structural dependency knowledge directly into the attention mechanism. Experiments conducted on a large-scale dataset derived from SmartBugs demonstrate that the proposed approach significantly outperforms traditional static tools and recent learning-based methods. TCRNet achieves an accuracy of 90.14% and a macro F1-score of 94.67%, showing substantial improvements over strong baselines. These results indicate that integrating semantic richness and structural awareness within a Contextual Encoding Network architecture enhances both detection reliability and generalization capability, making the framework suitable for practical smart contract security analysis.