EPED: A Novel Reinforcement Learning-Driven Framework for Early Phishing Scams Detection in Ethereum

Abstract

Ethereum’s active financial ecosystem makes itself become a hotbed of phishing scams. Existing studies construct transaction subgraphs and employ GNNs to identify potential phishing accounts. However, existing detection methods rely on complete historical transaction data, making it difficult to detect scams at an early stage. To address this issue, we propose Ethereum Phishing Scams Early Detection (called EPED) method. First, we combine GCN and GRU to capture accounts’ local structural relations and the temporal evolution of their transactions. Second, we introduce deep reinforcement learning for adaptive optimization of the detection time. The two strategies jointly enable early detection with limited data. Experimental results demonstrate that by using only 4.6 days of transaction data, our method achieves a Recall 4.09% higher than existing methods that rely on the full dataset. This result demonstrates the method’s timeliness and effectiveness.