A vulnerability detection framework with enhanced graph feature learning

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Vulnerability detection in smart contracts is critical to secure blockchain systems. Existing methods represent the bytecode as a graph structure and leverage graph neural networks to learn graph features for vulnerability detection. However, these methods are limited to handling the long-range dependencies between nodes. This means that they might focus on learning local node feature while ignoring global node information. In this paper, we propose a novel vulnerability detection framework with Enhanced Graph Feature Learning (EGFL), which aims to extract the global node information and utilize it to improve vulnerability detection in smart contracts. Specifically, we first represent the bytecode as a Control Flow Graph (CFG). To extract global node information, EGFL constructs a linear node feature matrix from CFG, and uses the feature-aware and relationship-aware modules to handle long-range dependencies between nodes. Meanwhile, a graph neural network is adopted to extract the local node feature from CFG. Subsequently, we fuse the global node information and local node feature to generate an enhanced graph feature for capturing more vulnerability features. We evaluate EGFL on the benchmark dataset with six types of smart contract vulnerabilities. Results show that EGFL outperforms fourteen state-of-the-art vulnerability detection methods by 10.83%–60.28% in F1 score.