Enhancing Ethereum Fraud Detection via Generative and Contrastive Self-supervision

TL;DR

This paper introduces Meta-IFD, a novel self-supervised framework that uses generative and contrastive learning to improve Ethereum fraud detection by addressing data imbalance and behavior pattern differentiation.

Contribution

It proposes the concept of meta-interactions and a dual self-supervision framework that enhances fraud detection accuracy on Ethereum data.

Findings

Effective detection of Ponzi schemes and phishing scams.

Generative module alleviates data imbalance.

Contrastive module improves behavior pattern differentiation.

Abstract

The rampant fraudulent activities on Ethereum hinder the healthy development of the blockchain ecosystem, necessitating the reinforcement of regulations. However, multiple imbalances involving account interaction frequencies and interaction types in the Ethereum transaction environment pose significant challenges to data mining-based fraud detection research. To address this, we first propose the concept of meta-interactions to refine interaction behaviors in Ethereum, and based on this, we present a dual self-supervision enhanced Ethereum fraud detection framework, named Meta-IFD. This framework initially introduces a generative self-supervision mechanism to augment the interaction features of accounts, followed by a contrastive self-supervision mechanism to differentiate various behavior patterns, and ultimately characterizes the behavioral representations of accounts and mines potential fraud risks through multi-view interaction feature learning. Extensive experiments on real Ethereum datasets demonstrate the effectiveness and superiority of our framework in detecting common Ethereum fraud behaviors such as Ponzi schemes and phishing scams. Additionally, the generative module can effectively alleviate the interaction distribution imbalance in Ethereum data, while the contrastive module significantly enhances the framework's ability to distinguish different behavior patterns. The source code will be available in https://github.com/GISec-Team/Meta-IFD.