Smart contract vulnerability

We propose a deep learning-based dataset for smart contract vulnerability detection, combining three public datasets to support and facilitate blockchain security research. This comprehensive dataset includes a variety of common types of smart contract vulnerabilities, such as re-entrancy attacks, integer overflows, and improper access controls.

By consolidating and uniformly annotating the data, we provide detailed vulnerability information and classification tags for each smart contract. The main features and contributions of the dataset are as follows: