Crypto Pump and Dump Detection via Deep Learning Techniques
This addresses cryptocurrency fraud detection, an under-researched area, but is incremental as it applies existing neural network architectures to a new domain.
The paper tackles the problem of detecting pump and dump schemes in cryptocurrencies by applying deep learning techniques, showing that these methods significantly outperform existing detection approaches.
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.