TTAGN: Temporal Transaction Aggregation Graph Network for Ethereum Phishing Scams Detection

TL;DR

This paper introduces TTAGN, a novel graph neural network that models temporal transaction relationships on Ethereum to improve phishing scam detection accuracy, outperforming existing methods.

Contribution

The paper proposes a new temporal transaction aggregation graph network that captures temporal relationships and trading features for enhanced Ethereum phishing detection.

Findings

Achieved 92.8% AUC and 81.6% F1-score on real-world datasets.

Outperformed state-of-the-art phishing detection methods.

Demonstrated effectiveness of temporal edge representation and edge2node modules.

Abstract

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.