Smart contract vulnerability detection based on BERT-based multilayer feature fusion

Abstract

As an important part of blockchain technology, smart contracts have attracted strong interest from industry and academia. They provide the foundation for implementing various blockchain applications and play a key role in the blockchain ecosystem. However, the frequent occurrence of smart contract vulnerabilities has resulted in significant economic losses and serious damage to the blockchain-based credit system. Currently, the security and reliability of smart contracts have become an emerging research field. Recently, deep learning methods have achieved certain results in mitigating the vulnerability problem of smart contracts, with the BERT model being widely used due to its good performance. However, the existing BERT model solely relies on features extracted from the last layer, leading to incomplete classification features. To address this issue, we propose a multi-layer feature fusion model that can accurately identify smart contract vulnerabilities. Our fusion model integrates features from multiple layers of the BERT model and employs a series of fusion strategies to enhance the comprehensiveness and accuracy of the extracted features. We extensively tested and verified the effectiveness and performance advantages of our proposed multi-layer feature fusion model. Compared to traditional single-layer feature extraction methods, our model demonstrates higher accuracy and a lower false positive rate in identifying smart contract vulnerabilities.