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, whichhas a very negative impact. To help to deal with this issue and to provide reusable research data sets forfuture research, this paper collects real-world samples and proposes an approach to detect Ponzi schemesimplemented as smart contracts (i.e., smart Ponzi schemes) on the blockchain. First, 200 smart Ponzischemes are obtained by manually checking more than 3,000 open source smart contracts on the Ethereumplatform. Then, two kinds of features are extracted from the transaction history and operation codes of thesmart contracts. Finally, a classi cation model is presented to detect smart Ponzi schemes. The extensiveexperiments show that the proposed model performs better than many traditional classi cation models andcan achieve high accuracy for practical use. By using the proposed approach, we estimate that there are morethan 500 smart Ponzi schemes running on Ethereum. Based on these results, we propose to build a uniformplatform to evaluate and monitor every created smart contract for early warning of scams.
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