Dual-channel Early Warning Framework for Ethereum Ponzi Schemes
Jie Jin, Jiajun Zhou, Chengxiang Jin, Shanqing Yu, Ziwan Zheng, Qi, Xuan

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…
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Taxonomy
TopicsBlockchain Technology Applications and Security · Imbalanced Data Classification Techniques · Cybercrime and Law Enforcement Studies
