Temporal Weighted Heterogeneous Multigraph Embedding for Ethereum Phishing Scams Detection

Abstract

Ethereum phishing scams have proven to be highly profitable in recent years, and pose a serious risk to the security of the blockchain ecosystem. Existing techniques for detecting phishing scams mostly model the transaction network at a very coarse-grained level. These methods rarely take into account the heterogeneity of the network, and do not consider multiple transactions over time between pairs of accounts. To this end, we model the Ethereum transaction network as a heterogeneous muiltidigraph and propose a novel graph embedding technique. Specifically, we use a temporal-weighted biased walking method based on the Jump-Stay strategy, which not only captures the properties of the dynamic transaction network more comprehensively, but also elegantly balances the distribution of different types of nodes. The superior performance of our model is shown through classification experiments on a real-world dataset of Ethereum transactions.