Multi-triplet Feature Augmentation for Ponzi Scheme Detection in Ethereum

TL;DR

This paper introduces MAHGNN, a novel graph neural network that leverages multi-triplet interaction patterns and semantic edge attributes to improve Ponzi scheme detection on Ethereum, achieving state-of-the-art results.

Contribution

It proposes a multi-triplet augmented heterogeneous graph neural network framework with CVAE for capturing semantic edge information, enhancing detection accuracy.

Findings

MAHGNN outperforms existing methods in Ponzi scheme detection.

The use of multi-triplet interaction patterns improves graph representation.

Semantic edge attributes significantly enhance detection performance.

Abstract

Blockchain technology revolutionizes the Internet, but also poses increasing risks, particularly in cryptocurrency finance. On the Ethereum platform, Ponzi schemes, phishing scams, and a variety of other frauds emerge. Existing Ponzi scheme detection approaches based on heterogeneous transaction graph modeling leverages semantic information between node (account) pairs to establish connections, overlooking the semantic attributes inherent to the edges (interactions). To overcome this, we construct heterogeneous Ethereum interaction graphs with multiple triplet interaction patterns to better depict the real Ethereum environment. Based on this, we design a new framework named multi-triplet augmented heterogeneous graph neural network (MAHGNN) for Ponzi scheme detection. We introduce the Conditional Variational Auto Encoder (CVAE) to capture the semantic information of different triplet interaction patterns, which facilitates the characterization on account features. Extensive experiments demonstrate that MAHGNN is capable of addressing the problem of multi-edge interactions in heterogeneous Ethereum interaction graphs and achieving state-of-the-art performance in Ponzi scheme detection.