  In recent years, phishing scams have become the most serious type of crime
involved in Ethereum, the second-largest blockchain platform. The existing
phishing scams detection technology on Ethereum mostly uses traditional machine
learning or network representation learning to mine the key information from
the transaction network to identify phishing addresses. However, these methods
adopt the last transaction record or even completely ignore these records, and
only manual-designed features are taken for the node representation. In this
paper, we propose a Temporal Transaction Aggregation Graph Network (TTAGN) to
enhance phishing scams detection performance on Ethereum. Specifically, in the
temporal edges representation module, we model the temporal relationship of
historical transaction records between nodes to construct the edge
representation of the Ethereum transaction network. Moreover, the edge
representations around the node are aggregated to fuse topological interactive
relationships into its representation, also named as trading features, in the
edge2node module. We further combine trading features with common statistical
and structural features obtained by graph neural networks to identify phishing
addresses. Evaluated on real-world Ethereum phishing scams datasets, our TTAGN
(92.8% AUC, and 81.6% F1score) outperforms the state-of-the-art methods, and
the effectiveness of temporal edges representation and edge2node module is also
demonstrated.
