Scalable anomaly detection in blockchain using graphics processing unit. (June 2021) - Morishima, Shin
Abstract: In blockchain, approved transactions, including illegal ones, cannot be modified unlike existing bank transactions. To prevent the damage caused by illegal transactions, rapid anomaly detection of transactions is required because transactions can be modified before approval. However, existing anomaly detection methods must process all transactions in blockchain, and the processing time is longer than the interval of each approval. In this paper, we propose a subgraph-based anomaly detection method to perform the detection using a part of the blockchain data. The proposed structure of the subgraph is suitable for graphics processing units (GPUs) to accelerate detection by using parallel processing. In an evaluation using real Bitcoin transaction data, when the number of targeted transactions was one hundred, the proposed method was 11.1x faster than an existing GPU-based method without lowering the detection accuracy. Graphical abstract: Highlights: Blockchain requires rapid anomaly detection to reduce damage of illegal transactions. Subgraph-based method prevents increase in computation cost as transactions increase. Parallelization of the graphics processing unit accelerates anomaly detection. MergedTRS enables anomaly detection for Bitcoin 500 transactions within two seconds.