\textsc{“Perseus”}: Tracing the Masterminds Behind Cryptocurrency Pump-and-Dump Schemes

ArXiv ID: 2503.01686 “View on arXiv”

Authors: Unknown

Abstract

Masterminds are entities organizing, coordinating, and orchestrating cryptocurrency pump-and-dump schemes, a form of trade-based manipulation undermining market integrity and causing financial losses for unwitting investors. Previous research detects pump-and-dump activities in the market, predicts the target cryptocurrency, and examines investors and \ac{“osn”} entities. However, these solutions do not address the root cause of the problem. There is a critical gap in identifying and tracing the masterminds involved in these schemes. In this research, we develop a detection system \textsc{“Perseus”}, which collects real-time data from the \acs{“osn”} and cryptocurrency markets. \textsc{“Perseus”} then constructs temporal attributed graphs that preserve the direction of information diffusion and the structure of the community while leveraging \ac{“gnn”} to identify the masterminds behind pump-and-dump activities. Our design of \textsc{“Perseus”} leads to higher F1 scores and precision than the \ac{“sota”} fraud detection method, achieving fast training and inferring speeds. Deployed in the real world from February 16 to October 9 2024, \textsc{“Perseus”} successfully detects $438$ masterminds who are efficient in the pump-and-dump information diffusion networks. \textsc{“Perseus”} provides regulators with an explanation of the risks of masterminds and oversight capabilities to mitigate the pump-and-dump schemes of cryptocurrency.

Keywords: Fraud Detection, Graph Neural Networks, Cryptocurrency, Market Manipulation, Temporal Graphs

Complexity vs Empirical Score

• Math Complexity: 2.5/10
• Empirical Rigor: 8.5/10
• Quadrant: Street Traders
• Why: The paper applies Graph Neural Networks (GNNs) and temporal graph modeling, which involves moderate mathematical complexity, but the focus is overwhelmingly on system implementation, real-world deployment, and extensive empirical validation. It features detailed data collection, large-scale real-time deployment, and high-performance metrics (F1, precision), indicating a practical, backtest-ready system.

  flowchart TD
    A["Research Goal<br>Identify & Trace Masterminds<br>Behind Crypto Pump-and-Dump Schemes"] --> B["Data Collection & Preprocessing<br>Real-time OSN & Market Data"]
    B --> C["Temporal Graph Construction<br>Attributed Graphs preserving<br>Info Diffusion & Community Structure"]
    C --> D["GNN-based Detection<br>Leveraging Graph Neural Networks<br>to Identify Masterminds"]
    D --> E["Real-World Deployment<br>Feb 16 - Oct 9, 2024"]
    E --> F{"Key Outcomes"}
    F --> F1["Detected 438 Masterminds<br>Efficient in Diffusion Networks"]
    F --> F2["Higher F1 & Precision vs SOTA<br>Fast Training & Inference"]
    F --> F3["Risk Explanation & Oversight<br>Tools for Regulators"]