Security Analysis of Ponzi Schemes in Ethereum Smart Contracts

2025-10-04

AI Summary

To address the challenges of low detection accuracy and inefficiency in identifying Ponzi schemes within Ethereum smart contracts, this paper proposes an automated, program-analysis-based detection framework. First, through reverse engineering and case studies, we systematically identify four structural patterns and code-level commonalities characteristic of Ponzi scheme contracts. Second, we integrate static and dynamic analysis using Mythril, augmented by custom shell scripts to enable large-scale scanning and behavioral feature extraction from open-source contracts. Finally, we construct a lightweight detection pipeline that successfully identifies multiple previously unknown Ponzi schemes on real-world datasets, demonstrating high precision and scalability. Our framework provides a reproducible methodology and practical toolset for proactive defense against on-chain financial fraud.

Technology Category

• Data Mining & Knowledge Management: Anomaly/Outlier Detection
• Machine Learning: Evaluation and Analysis
• Natural Language Processing: Fact-Checking / Misinformation Detection (NLP Focus)

Application Category

• Security and Privacy: Cryptocurrency and smart contracts
• Web Mining and Content Analysis: Web data provenance, reliability, and authenticity
• Economics, Online Markets and Human Computation: Sustainability of Web economics

Abstract

The rapid advancement of blockchain technology has precipitated the widespread adoption of Ethereum and smart contracts across a variety of sectors. However, this has also given rise to numerous fraudulent activities, with many speculators embedding Ponzi schemes within smart contracts, resulting in significant financial losses for investors. Currently, there is a lack of effective methods for identifying and analyzing such new types of fraudulent activities. This paper categorizes these scams into four structural types and explores the intrinsic characteristics of Ponzi scheme contract source code from a program analysis perspective. The Mythril tool is employed to conduct static and dynamic analyses of representative cases, thereby revealing their vulnerabilities and operational mechanisms. Furthermore, this paper employs shell scripts and command patterns to conduct batch detection of open-source smart contract code, thereby unveiling the common characteristics of Ponzi scheme smart contracts.

Problem

• Identifying Ponzi schemes in Ethereum smart contracts
• Analyzing vulnerabilities and mechanisms of fraudulent contracts
• Developing detection methods for Ponzi scheme characteristics

Innovation

• Classified Ponzi schemes into four structural types
• Used Mythril tool for static and dynamic analysis
• Employed shell scripts for batch detection patterns