  Blockchain technology supports the generation and record of transactions, and
maintains the fairness and openness of the cryptocurrency system. However, many
fraudsters utilize smart contracts to create fraudulent Ponzi schemes for
profiting on Ethereum, which seriously affects financial security. Most
existing Ponzi scheme detection techniques suffer from two major restricted
problems: the lack of motivation for temporal early warning and failure to fuse
multi-source information finally cause the lagging and unsatisfactory
performance of Ethereum Ponzi scheme detection. In this paper, we propose a
dual-channel early warning framework for Ethereum Ponzi schemes, named
Ponzi-Warning, which performs feature extraction and fusion on both code and
transaction levels. Moreover, we represent a temporal evolution augmentation
strategy for generating transaction graph sequences, which can effectively
increase the data scale and introduce temporal information. Comprehensive
experiments on our Ponzi scheme datasets demonstrate the effectiveness and
timeliness of our framework for detecting the Ponzi contract accounts.
