Blockchain is a distributed ledger system composed of a P2P network and is used for a wide range of applications, such as international remittance, inter-individual transactions, and asset conservation. In Blockchain systems, tamper resistance is enhanced by the property of transaction that cannot be changed or deleted by everyone including the creator of the transaction. However, this property also becomes a problem that unintended transaction created by miss operation or secret key theft cannot be corrected later. Due to this problem, once an illegal transaction such as theft occurs, the damage will expand. To suppress the damage, we need countermeasures, such as detecting illegal transaction at high speed and correcting the transaction before approval. However, anomaly detection in the Blockchain at high speed is computationally heavy, because we need to repeat the detection process using various feature quantities and the feature extractions become overhead. In this paper, to accelerate anomaly detection, we propose to cache transaction information necessary for extracting feature in GPU device memory and perform both feature extraction and anomaly detection in the GPU. We also propose a conditional feature extraction method to reduce computation cost of anomaly detection. We employ anomaly detection using K-means algorithm based on the conditional features. When the number of users is one million and the number of transactions is 100 millions, our proposed method achieves 8.6 times faster than CPU processing method and 2.6 times faster than GPU processing method that does not perform feature extraction on the GPU. In addition, the conditional feature extraction method achieves 1.7 times faster than the unconditional method when the number of users satisfying a given condition is 200 thousands out of one million.

Engineers and researchers have recently paid attention to Blockchain. Blockchain is a fault-tolerant distributed ledger without administrators. Blockchain is originally derived from cryptocurrency, but it is possible to be applied to other industries. Transferring digital asset is called a transaction. Blockchain holds all transactions, so the total amount of Blockchain data will increase as time proceeds. On the other hand, the number of Internet of Things (IoT) products has been increasing. It is difficult for IoT products to hold all Blockchain data because of their storage capacity. Therefore, they access Blockchain data via servers that have Blockchain data. However, if a lot of IoT products access Blockchain network via servers, server overloads will occur. Thus, it is useful to reduce workloads and improve throughput. In this paper, we propose a caching technique using a Field Programmable Gate Array-based (FPGA) Network Interface Card (NIC) which possesses four 10Gigabit Ethernet (10GbE) interfaces. The proposed system can reduce server overloads, because the FPGA NIC instead of the server responds to requests from IoT products if cache hits. We implemented the proposed hardware cache to achieve high throughput on NetFPGA-10G board. We counted the number of requests that the server or the FPGA NIC processed as an evaluation. As a result, the throughput improved by on average 1.97 times when hitting the cache.