  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.
