Smart Contracts Vulnerability Detection Using Machine Learning and Large Language Models

Abstract

As blockchain technology and smart contracts gain widespread adoption, ensuring their security is essential to prevent financial and operational risks. Detecting vulnerabilities in smart contracts using automated techniques provides a reliable and scalable solution. This study utilizes the Smart Contract Vulnerabilities Dataset from Kaggle, containing annotated smart contracts with labeled vulnerabilities. Preprocessing includes tokenization and exploratory data analysis to extract meaningful textual patterns. Deep learning models such as LSTM and BERT are trained and evaluated using accuracy, precision, recall, and F1-score. To further improve detection performance, BERT embeddings are combined with BiLSTM and CNN + LSTM architectures. A Flask-based user interface enables real-time vulnerability prediction. Experimental results show that the CNN + LSTM model outperforms all other models, achieving 95 percent accuracy and demonstrating strong capability in identifying smart contract vulnerabilities.