PonziLens+: Visualizing Bytecode Actions for Smart Ponzi Scheme Identification

2024-12-24

IEEE Transactions on Visualization and Computer Graphics

AI Summary

Detecting Ponzi schemes in blockchain smart contracts remains challenging due to their structural complexity and poor interpretability. Method: This paper proposes the first detection framework leveraging semantic action extraction from EVM bytecode—statically identifying interpretable operations (e.g., fund aggregation, tiered returns)—integrated with dynamic behavioral modeling. It introduces a three-tier interactive visualization module: temporal fund-flow graphs, control-flow heatmaps, and fraud-feature radar charts, enabling human-in-the-loop verification. Contribution/Results: The framework achieves explainable and verifiable identification across diverse Ponzi contract types without requiring domain-specific prior knowledge, facilitating rapid assessment of novel scams. Evaluated via double-blind assessment by 12 domain experts and investors, it demonstrates significantly improved detection accuracy and enhanced decision-making confidence.

Technology Category

• Data Mining & Knowledge Management: Representing, Reasoning, and Using Provenance, Trust
• Natural Language Processing: Fact-Checking / Misinformation Detection (NLP Focus)
• Reasoning under Uncertainty: Other Foundations of Reasoning under Uncertainty

Application Category

• Security and Privacy: Cryptocurrency and smart contracts
• Economics, Online Markets and Human Computation: Economic aspects of blockchain and cryptocurrencies
• Web Mining and Content Analysis: Models for Web evolution

Abstract

With the prevalence of smart contracts, smart Ponzi schemes have become a common fraud on blockchain and have caused significant financial loss to cryptocurrency investors in the past few years. Despite the critical importance of detecting smart Ponzi schemes, a reliable and transparent identification approach adaptive to various smart Ponzi schemes is still missing. To fill the research gap, we first extract semantic-meaningful actions to represent the execution behaviors specified in smart contract bytecodes, which are derived from a literature review and in-depth interviews with domain experts. We then propose PonziLens+, a novel visual analytic approach that provides an intuitive and reliable analysis of Ponzi-scheme-related features within these execution behaviors. PonziLens+ has three visualization modules that intuitively reveal all potential behaviors of a smart contract, highlighting fraudulent features across three levels of detail. It can help smart contract investors and auditors achieve confident identification of any smart Ponzi schemes. We conducted two case studies and in-depth user interviews with 12 domain experts and common investors to evaluate PonziLens+. The results demonstrate the effectiveness and usability of PonziLens+ in achieving an effective identification of smart Ponzi schemes.

Problem

• Blockchain
• Smart Ponzi Scheme
• Detection Method

Innovation

• PonziLens+
• Visualization Analysis Tool
• Smart Contract Fraud Detection