2DynEthNet: A Two-Dimensional Streaming Framework for Ethereum Phishing Scam Detection

Abstract

In recent years, phishing scams have emerged as one of the most serious crimes on Ethereum. Existing phishing scam detection methods typically model public transaction records on the blockchain as a graph, and then identify phishing addresses through manual feature extraction or graph learning frameworks. Meanwhile, these methods model transactions within a period as a static network for analysis. Therefore, these methods lack the ability to capture fine-grained time dynamics, and on the other hand, they cannot handle the large-scale and continuously growing transaction data on the Ethereum blockchain, resulting in lower scalability and efficiency. In this paper, we propose a two-dimensional streaming framework 2DynEthNet for Ethereum phishing scam detection. First, we cast the transaction series into 6 slices according to block numbers, treating each as a separate task. In the first dimension, we treat transaction features as edge features instead of node features within one task, allowing each transaction to be streamed in 2DynEthNet, aiming to capture the evolutionary features of the Ethereum transaction network at a fine-grained level in continuous time. In the second dimension, we adopt the strategy of incremental information training between tasks, which utilizes meta-learning to quickly update the model parameters under new slices, thus effectively improving the scalability of the model. Finally, experimental results on large-scale real Ethereum phishing scam datasets show that our 2DynEthNet outperforms the state-of-the-art methods with 28.44% average Recall and achieves the most efficient training speed, proving the effectiveness of both temporal edge representation and meta-learning. In addition, we provide an Ethereum large-scale dynamic graph transaction dataset, ETGraph, which aligns with the data distribution in real transaction scenarios without sampling and filtering unlabeled accounts.