# Crypto Pump and Dump Detection via Deep Learning Techniques

arXiv: https://arxiv.org/abs/2205.04646 · HTML: https://mlsanity.com/paper/2205.04646/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 21 references that are in the map.
- First posted on arXiv 10 May 2022.

## Builds on (most cited first, 5 of 5)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Anomaly Transformer: Time Series Anomaly Detection with Association Discrepancy](https://mlsanity.com/paper/2110.02642/) (2021)
- [LSTM-based Encoder-Decoder for Multi-sensor Anomaly Detection](https://mlsanity.com/paper/1607.00148/) (2016)
- [Recurrent Neural Network Attention Mechanisms for Interpretable System Log Anomaly Detection](https://mlsanity.com/paper/1803.04967/) (2018)
- [Pump and Dumps in the Bitcoin Era: Real Time Detection of Cryptocurrency Market Manipulations](https://mlsanity.com/paper/2005.06610/) (2020)

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

- [textscPerseus: Tracing the Masterminds Behind Cryptocurrency Pump-and-Dump Schemes](https://mlsanity.com/paper/2503.01686/) (2025)
- [Catching the Rug: Early Prediction of Fraudulent Memecoins on Solana via Machine Learning](https://mlsanity.com/paper/2608.20271/) (2026)

## Abstract (arXiv)

Despite the fact that cryptocurrencies themselves have experienced an astonishing rate of adoption over the last decade, cryptocurrency fraud detection is a heavily under-researched problem area. Of all fraudulent activity regarding cryptocurrencies, pump and dump schemes are some of the most common. Though some studies have been done on these kinds of scams in the stock market, the lack of labelled stock data and the volatility unique to the cryptocurrency space constrains the applicability of studies on the stock market toward this problem domain. Furthermore, the only work done in this space thus far has been either statistical in nature, or has been concerned with classical machine learning models such as random forest trees. We propose the novel application of two existing neural network architectures to this problem domain and show that deep learning solutions can significantly outperform all other existing pump and dump detection methods for cryptocurrencies.