Dual-channel Early Warning Framework for Ethereum Ponzi Schemes

TL;DR

This paper introduces Ponzi-Warning, a dual-channel framework that fuses code and transaction data with temporal evolution strategies to improve early detection of Ethereum Ponzi schemes.

Contribution

The paper presents a novel dual-channel framework that combines code and transaction features with temporal data augmentation for timely Ponzi scheme detection.

Findings

Effective detection of Ponzi contracts demonstrated

Improved timeliness over existing methods

Enhanced data scale through temporal augmentation

Abstract

Blockchain technology supports the generation and record of transactions, and maintains the fairness and openness of the cryptocurrency system. However, many fraudsters utilize smart contracts to create fraudulent Ponzi schemes for profiting on Ethereum, which seriously affects financial security. Most existing Ponzi scheme detection techniques suffer from two major restricted problems: the lack of motivation for temporal early warning and failure to fuse multi-source information finally cause the lagging and unsatisfactory performance of Ethereum Ponzi scheme detection. In this paper, we propose a dual-channel early warning framework for Ethereum Ponzi schemes, named Ponzi-Warning, which performs feature extraction and fusion on both code and transaction levels. Moreover, we represent a temporal evolution augmentation strategy for generating transaction graph sequences, which can effectively increase the data scale and introduce temporal information. Comprehensive experiments on our Ponzi scheme datasets demonstrate the effectiveness and timeliness of our framework for detecting the Ponzi contract accounts.