Dissertation (February 18, 2026): Matheus dos Santos Moura
Student: Matheus dos Santos Moura
Title: Hybrid Anomaly and Change Point Detection for Pump-and-Dump Schemes in Centralized Cryptocurrency Exchanges
Advisors: Diogo Silveira Mendonça
Committee: Diogo Silveira Mendonça (Cefet/RJ), Eduardo Soares Ogasawara (Cefet/RJ) and Igor Machado Coelho (UFF)
Day/Hour: February 18, 2026 / 3 p.m.
Abstract: The rapid growth of cryptocurrency markets has intensified concerns regarding market manipulation practices, particularly pump-and-dump schemes. Detecting such schemes remains challenging due to the high volatility of cryptocurrencies and the limited availability of reliable ground-truth data. Prior work has predominantly relied on anomaly detection techniques, which often exhibit limited precision and adaptability. In this work, we propose two offline statistical methods that explore a hybrid framework combining anomaly detection and change point detection for pump-and-dump detection. The first method, HD Pump, integrates volatility anomaly detection in price time series with change point detection applied to trading volume. The second method, HD Pump Plus, extends this approach by replacing the price time series with a rush-order-based time series. Experimental evaluation on a dataset of 178 confirmed pump-and-dump events from the Binance exchange shows that HD Pump Plus outperforms prior statistical approaches, achieving a precision of 96.4%, recall of 89.3%, and F1-score of 92.7%. These results demonstrate the effectiveness of hybrid detection strategies in advancing the state of the art while maintaining methodological simplicity.