Directed dynamic attribute graph anomaly detection based on evolved graph attention for blockchain

Blockchain is gradually becoming an important data storage platform for Internet digital copyright confirmation, electronic deposit, and data sharing. Anomaly detection on the blockchain has received extensive attention as the foundation for securing blockchain-based digital applications. However, the current blockchain anomaly detection for obtaining network nodes' depth and dynamic change features still needs improvement. In this paper, we propose a public blockchain anomaly detection method based on evolved graph attention. Different from general blockchain network modeling methods, we first adopt a dynamic attribute graph network construction method to model each transaction using edges to provide more learnable transaction attribute information for graph representation learning in blockchain networks. Then, we propose an evoluted graph attention network structure to fully extract the deep features of blockchain nodes by learning the temporal evolution characteristics of blockchain networks and dynamically updating the node learning weights of subgraphs in different timestamps. In order to solve the dataset imbalance problem, we also apply the GraphSMOTE method for graph-structured data on public blockchain networks for the first time. Finally, we identify node labels in blockchain networks using a binary classification method and verify our proposed scheme through multiple rounds of experiments.