Phishing Detection on Ethereum via Attributed Ego-Graph Embedding

In recent years, the losses caused by phishing scams on Ethereum have reached a level that cannot be ignored. In such a phishing detection scenario, network embedding is seen as an effective solution. In this brief, we propose an attributed ego-graph embedding framework to distinguish phishing accounts. We first obtain the account labels from an authority site and the transaction records from Ethereum on-chain blocks. Then we extract ego-graphs for each labeled account to represent it. To learn representations for ego-graphs, we utilize non-linear substructures sampled from ego-graphs and use a skip-gram model. Finally, a classifier is applied to graph embeddings to predict phishing accounts. To overcome the limit that transaction attributes are not encoded into ego-graph embeddings, we give nodes and subgraphs with richer attribute-based semantics. Specifically, we propose a novel node relabeling strategy based on Ethereum transaction attributes including transaction amount, number, and direction, and differentiating nodes and subgraphs by new labels. Through this, structural and attributed features of the Ethereum transaction networks can be learned at the same time. Experimental results show that our framework achieves effective performance on class imbalanced phishing detection on Ethereum.