  The Ponzi scheme, an old-fashioned fraud, is now popular on the Ethereum
blockchain, causing considerable financial losses to many crypto investors. A
few Ponzi detection methods have been proposed in the literature, most of which
detect a Ponzi scheme based on its smart contract source code. This
contract-code-based approach, while achieving very high accuracy, is not robust
because a Ponzi developer can fool a detection model by obfuscating the opcode
or inventing a new profit distribution logic that cannot be detected. On the
contrary, a transaction-based approach could improve the robustness of
detection because transactions, unlike smart contracts, are harder to be
manipulated. However, the current transaction-based detection models achieve
fairly low accuracy. In this paper, we aim to improve the accuracy of the
transaction-based models by employing time-series features, which turn out to
be crucial in capturing the life-time behaviour a Ponzi application but were
completely overlooked in previous works. We propose a new set of 85 features
(22 known account-based and 63 new time-series features), which allows
off-the-shelf machine learning algorithms to achieve up to 30% higher F1-scores
compared to existing works.
