Heterogeneous Feature Augmentation for Ponzi Detection in Ethereum

TL;DR

This paper introduces HFAug, a module that enhances Ponzi scheme detection in Ethereum by incorporating heterogeneous features, significantly improving existing detection methods' performance through leveraging diverse account behavior patterns.

Contribution

The paper proposes a novel Heterogeneous Feature Augmentation module that captures and integrates heterogeneous information to improve Ponzi scheme detection in blockchain.

Findings

HFAug significantly improves detection accuracy.

Heterogeneous features enhance pattern recognition.

Experimental results confirm effectiveness on Ethereum data.

Abstract

While blockchain technology triggers new industrial and technological revolutions, it also brings new challenges. Recently, a large number of new scams with a "blockchain" sock-puppet continue to emerge, such as Ponzi schemes, money laundering, etc., seriously threatening financial security. Existing fraud detection methods in blockchain mainly concentrate on manual feature and graph analytics, which first construct a homogeneous transaction graph using partial blockchain data and then use graph analytics to detect anomaly, resulting in a loss of pattern information. In this paper, we mainly focus on Ponzi scheme detection and propose HFAug, a generic Heterogeneous Feature Augmentation module that can capture the heterogeneous information associated with account behavior patterns and can be combined with existing Ponzi detection methods. HFAug learns the metapath-based behavior characteristics in an auxiliary heterogeneous interaction graph, and aggregates the heterogeneous features to corresponding account nodes in the homogeneous one where the Ponzi detection methods are performed. Comprehensive experimental results demonstrate that our HFAug can help existing Ponzi detection methods achieve significant performance improvement on Ethereum datasets, suggesting the effectiveness of heterogeneous information on detecting Ponzi schemes.