# textscPerseus: Tracing the Masterminds Behind Cryptocurrency Pump-and-Dump Schemes

arXiv: https://arxiv.org/abs/2503.01686 · HTML: https://mlsanity.com/paper/2503.01686/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 37 references that are in the map.
- First posted on arXiv 3 Mar 2025.

## Builds on (most cited first, 16 of 16)

- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [Inductive Representation Learning on Temporal Graphs](https://mlsanity.com/paper/2002.07962/) (2020)
- [A Survey on Deep Learning for Named Entity Recognition](https://mlsanity.com/paper/1812.09449/) (2018)
- [Temporal Graph Benchmark for Machine Learning on Temporal Graphs](https://mlsanity.com/paper/2307.01026/) (2023)
- [The Anatomy of a Cryptocurrency Pump-and-Dump Scheme](https://mlsanity.com/paper/1811.10109/) (2018)
- [The Doge of Wall Street: Analysis and Detection of Pump and Dump Cryptocurrency Manipulations](https://mlsanity.com/paper/2105.00733/) (2021)
- [Identifying and Analyzing Cryptocurrency Manipulations in Social Media](https://mlsanity.com/paper/1902.03110/) (2019)
- [Cascading Machine Learning to Attack Bitcoin Anonymity](https://mlsanity.com/paper/1910.06560/) (2019)
- [Detecting Malicious Accounts in Permissionless Blockchains using Temporal Graph Properties](https://mlsanity.com/paper/2007.05169/) (2020)
- [Pump and Dumps in the Bitcoin Era: Real Time Detection of Cryptocurrency Market Manipulations](https://mlsanity.com/paper/2005.06610/) (2020)
- [Sequence-Based Target Coin Prediction for Cryptocurrency Pump-and-Dump](https://mlsanity.com/paper/2204.12929/) (2022)
- [Cloudburst: Stateful Functions-as-a-Service](https://mlsanity.com/paper/2001.04592/) (2020)
- [Joint Inference of Diffusion and Structure in Partially Observed Social Networks Using Coupled Matrix Factorization](https://mlsanity.com/paper/2010.01400/) (2020)
- [DANI: A Fast Diffusion Aware Network Inference Algorithm](https://mlsanity.com/paper/1706.00941/) (2017)
- [Crypto Pump and Dump Detection via Deep Learning Techniques](https://mlsanity.com/paper/2205.04646/) (2022)

## Built on by (most cited first, 2 of 2)

- [DLT-Corpus: A Large-Scale Text Collection for the Distributed Ledger Technology Domain](https://mlsanity.com/paper/2602.22045/) (2026)
- [SoK: Colluding Adversaries in Machine Learning Pipelines](https://mlsanity.com/paper/2606.10091/) (2026)

## Abstract (arXiv)

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 acosn 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 textscPerseus, which collects real-time data from the acsosn and cryptocurrency markets. textscPerseus then constructs temporal attributed graphs that preserve the direction of information diffusion and the structure of the community while leveraging acgnn to identify the masterminds behind pump-and-dump activities. Our design of textscPerseus leads to higher F1 scores and precision than the acsota fraud detection method, achieving fast training and inferring speeds. Deployed in the real world from February 16 to October 9 2024, textscPerseus successfully detects 438 masterminds who are efficient in the pump-and-dump information diffusion networks. textscPerseus provides regulators with an explanation of the risks of masterminds and oversight capabilities to mitigate the pump-and-dump schemes of cryptocurrency.