EXCT: An Improved TextRank-Based Smart Contract Vulnerability Detection Method

Abstract

The security issues surrounding smart contracts have garnered significant attention due to potential vulnerabilities that can lead to financial losses and a decline in trust. Despite the development of various vulnerability detection methods by researchers, existing models often suffer from low accuracy and high false positive rates. Additionally, opcode-based vulnerability detection methods frequently introduce excessive noise due to long sequences, impairing the model’s generalization capabilities. To address these challenges, this paper proposes a dual-branch vulnerability detection model, referred to as EXCT, which integrates features from both the original opcode sequences and significant opcode sequences. We employ an improved extractive summarization technique, KTextRank, to extract important opcode sequence segments. A hierarchical Transformer is utilized for global feature extraction, while Convolutional Neural Networks (CNN) are employed for local feature extraction from the original opcode sequences. Finally, we fuse the global and local features to effectively identify specific vulnerabilities within smart contract code. Experiments conducted on two real-world datasets demonstrate that our proposed approach significantly enhances performance on publicly available datasets.