Abnormal Transaction Detection based on Graph Networks

This paper proposes a deep learning approach to address the two issues in abnormal transaction detection, one is to process dynamically generated transaction data, instead of having to use the static and whole graph structure; and the other is to fully explore the information in the graph structure of transactions to improve the learning ability. The proposed approach is applied on the dataset provided by Elliptic, and the experiments evaluate the proposed approach by comparing with the existing ones the precision rate, recall rate, and F1-score indicators for detecting illegal transactions, increasing by 2%, 3.9% and 3.8%, respectively.