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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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The cryptocurrency market is a very huge market without effective supervision. It is of great importance for investors and regulators to recognize whether there are market manipulation and its manipulation patterns. This paper proposes an approach to mine the transaction networks of exchanges for answering this question. By taking the leaked transaction history of Mt. Gox Bitcoin exchange as a sample, we first divide the accounts into three categories according to its characteristic and then construct the transaction history into three graphs. Many observations and findings are obtained via analyzing the constructed graphs. To evaluate the influence of the accounts' transaction behavior on the Bitcoin exchange price, the graphs are reconstructed into series and reshaped as matrices. By using singular value decomposition (SVD) on the matrices, we identify many base networks which have a great correlation with the price fluctuation. When further analyzing the most important accounts in the base networks, plenty of market manipulation patterns are found. According to these findings, we conclude that there was serious market manipulation in Mt. Gox exchange and the cryptocurrency market must strengthen the supervision.