Elevating Smart Contract Defenses: A Coordinated NLP-Based Strategy for Vulnerability Detection

Abstract

With the continuous advancement of blockchain technology, smart contracts, as one of its core applications, have increasingly become a focal point for security concerns. To address this, this paper proposes a novel method that integrates keyword filter technology, the pre-trained Bidirectional Encoder Representations from Transformers (BERT) model, and the Bidirectional Long Short-Term Memory-Conditional Random Field (BiLSTM-CRF) architecture, aimed at enhancing the efficiency and accuracy of vulnerability detection in Solidity smart contracts. Initially, keyword filter technology is employed to select and preprocess code snippets, extracting features closely associated with security vulnerabilities. Subsequently, the BERT model conducts deep semantic analysis and feature extraction, after which the BiLSTM-CRF architecture further learns from the features and predicts vulnerability types. Extensive experiments conducted on a dataset comprising eight major types of vulnerabilities demonstrate that the method proposed in this study significantly outperforms existing vulnerability detection methods in key metrics such as accuracy, recall, and F1 scores. This research not only provides an effective technical solution for detecting security vulnerabilities in smart contracts but also holds significant theoretical and practical implications for promoting the safe and reliable development of blockchain technology.