Scalable Anomaly Detection Method for Blockchain Transactions using GPU

Abstract

Blockchain is a distributed ledger system composed of P2P network proposed as an electronic cash system which can transfer money without a trusted third party. Blockchain has high tamper resistance by the structure which cannot modify a transaction by everyone including the creator of it. However, it also becomes a problem that Blockchain system cannot modify fraudulent transaction already approved. This problem means once an illegal transaction occurs, the damage expands. It is necessary to detect the transaction by the anomaly detection and modify it before approval in order to suppress the damage. However, existing anomaly detection methods of Blockchain need the processing for all the past transactions in Blockchain. The execution time exceeds the approval interval of the major Blockchain system (Ethereum). In this paper, we propose an anomaly detection method using a fixed size user-centric subgraph which is extracted from whole graph made from all the transactions, to prevent the increase of the execution time. Furthermore, to accelerate the anomaly detections, we propose the subgraph structure which is suitable for GPU processing so that all of the subgraph making, the feature extraction, and the anomaly detection are performed in GPU. When the number of transactions is 300 million, our proposed method archives 195 times faster than the existing GPU-based method and the execution time is shorter than the approval interval of the Ethereum. In terms of accuracy, the true positive rate is significantly higher than the existing method in the case of small scale transactions because the local anomaly can be detected by the subgraph with locality. And the rate in the case of large scale and the false positive rate are close to the existing method.