Securing smart grids relies in part on the reliable integration of blockchain technologies for the automation of energy transactions. However, the presence of vulnerabilities in smart contracts poses a direct threat to the integrity and resilience of these critical systems. This work presents a unique and structured dataset of real-world vulnerabilities observed in smart contracts, intended for cybersecurity research applied to smart energy infrastructures.
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:
