Blockchain technology becomes increasingly popular. It also attracts scams, for example, a Ponzi scheme, a classic fraud, has been found making a notable amount of money on Blockchain, which has a very negative impact. To help to deal with this issue and to provide reusable research data sets for future research, this paper collects real-world samples and proposes an approach to detect Ponzi schemes implemented as smart contracts (i.e., smart Ponzi schemes) on the blockchain. First, 200 smart Ponzi schemes are obtained by manually checking more than 3,000 open source smart contracts on the Ethereum platform. Then, two kinds of features are extracted from the transaction history and operation codes of the smart contracts. Finally, a classification model is presented to detect smart Ponzi schemes. The extensive experiments show that the proposed model performs better than many traditional classification models and can achieve high accuracy for practical use. By using the proposed approach, we estimate that there are more than 500 smart Ponzi schemes running on Ethereum. Based on these results, we propose to build a uniform platform to evaluate and monitor every created smart contract for early warning of scams.

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Blockchain-based cryptocurrencies have received extensive attention recently. Massive data has been stored on permission-less blockchains. The analysis of massive blockchain data can bring huge business values. However, the absence of well-processed up-to-date blockchain datasets impedes big data analytics of blockchain data. To fill this gap, we collect and process the up-to-date on-chain data from Ethereum, which is one of the most popular permission-less blockchains. We name such well-processed Ethereum data as XBlock-ETH, which consists of transactions, smart contracts, and cryptocurrencies (i.e., tokens). However, it is non-trivial to partition and categorize the collected raw Ethereum data to the well-processed datasets since the whole processing procedure requires sophisticated knowledge on software engineering as well as big data analytics. Moreover, we also present basic statistics and exploration for each of the well-processed datasets. Furthermore, we also outline the possible research opportunities based on XBlock-ETH, with the data and code released online.