Crypto Pump and Dump via Deep Learning Techniques
원문 초록
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.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
EDINET-Bench: Evaluating LLMs on Complex Financial Tasks using Japanese Financial Statements
GARG-AML against Smurfing: A Scalable and Interpretable Graph-Based Framework for Anti-Money Laundering
AD-AGENT: A Multi-agent Framework for End-to-end Anomaly Detection
Cyber Security Data Science: Machine Learning Methods and their Performance on Imbalanced Datasets
Federated Spectral Graph Transformers Meet Neural Ordinary Differential Equations for Non-IID Graphs
FeatInsight: An Online ML Feature Management System on 4Paradigm Sage-Studio Platform
토론
이 논문과 연결된 포럼 글이 아직 없습니다.