ConvMHSA-SCVD: Enhancing Smart Contract Vulnerability Detection through a Knowledge-Driven and Data-Driven Framework

Smart contracts are essential for executing computing logic on blockchain networks. However, they are also susceptible to various vulnerabilities. In recent years, the detection of smart contract vulnerabilities has become a significant concern due to the substantial losses caused by hacker attacks. Traditional vulnerability detection approaches rely on expert rules, which often suffer from limitations in accuracy and completeness. Deep learning-based methods offer better coverage of vulnerabilities but may overlook certain vulnerability characteristics and suffer from overfitting during training. In this paper, we propose a novel approach called ConvMHSA-SCVD, which combines knowledge-driven and data-driven algorithms together to detect smart contract vulnerabilities. By incorporating feature selection, data balancing, and a combination of multi-channel convolution and multi-head self-attention neural networks, our ConvMHSA-SCVD achieves effective vulnerability detection in smart contracts. Extensive experiments demonstrate that our approach outperforms the state-of-the-art method in accuracy and F1 score, with improvements ranging from 0.4% to 3.84% and 1.28% to 1.90%, respectively.