Enhancing Smart Contract Vulnerability Detection via Dual-Source Feature Extraction and Fusion

Abstract

The pervasive adoption of smart contracts in blockchain has raised concerns about their vulnerabilities, which have led to serious economic losses. To address the efficiency and performance drawbacks of traditional methods, researchers have turned to deep learning techniques, designing various vulnerability detection methods using specific code information sources. However, these learning-based methods face limitations in feature modeling. Most emphasize feature extraction from either source code or bytecode, resulting in limited feature coverage and compromised vulnerability representation. While some attempt to utilize both code sources, they typically treat one as auxiliary, failing to perform effective joint alignment. To this end, we propose DualSVD, a dual-source feature modeling framework for smart contract vulnerability detection. DualSVD encodes vulnerability-relevant source code functions into semantic vectors using word embeddings, and extracts bytecode features using a channel architecture fused via channel-wise attention. Both feature representations are then projected into a shared latent space and concatenated for classification. We evaluate the proposed approach on widely-used datasets covering eight smart contract vulnerability types. Experimental results demonstrate that DualSVD achieves an average F1-score of 92.70%, outperforming traditional and deep learning-based baselines by 37.81% and 4.94%, respectively. These results indicate that DualSVD provides a more comprehensive and effective representation of smart contract vulnerabilities, offering improved detection performance and stronger generalization ability.