HDP+: Leveraging Anomaly and Change Point Detection for Pump-and-Dump in Cryptocurrency Exchanges
DOI:
https://doi.org/10.5753/jidm.2026.5728Keywords:
Cryptocurrency, Pump-and-dump, Fraud detection, Anomaly detection, Change point detectionAbstract
Cryptocurrencies are increasingly gaining relevance in financial markets, attracting both retail and institutional investors. As a result, assessing the risks associated with these new assets has become more important. Unlike traditional market assets, which are regulated and centrally managed, cryptocurrencies were designed with an egalitarian nature, enabling peer-to-peer transactions and providing varying levels of anonymity. These characteristics contribute to a favorable environment for illicit activities, including market manipulation. One such manipulation technique is the pump-and-dump (PD) scheme, which exploits the decentralized and anonymous nature of cryptocurrency markets. Typically orchestrated through public groups on social media platforms, these schemes involve coordinated surges in buy orders to artificially inflate a coin’s price, followed by rapid sell-offs that leave unsuspecting investors with losses. The detection of PD schemes is important because they compromise the integrity and reputation of cryptocurrency markets, undermining investor confidence and contributing to market instability. Most prior research on PD detection has focused on anomaly detection techniques. In this study, we investigate whether combining anomaly detection with change point detection in a hybrid framework can enhance detection performance. We propose HDP+, an improved version of the original HDP method, which processes raw trading records from exchanges and applies an ensemble of anomaly detection and change point detection algorithms. The primary distinction between HDP and HDP+ lies in the anomaly detection component: while HDP relies on traditional volatility-based methods, HDP+ analyzes the time series of rush orders. Experiments conducted on a dataset of confirmed PD events yielded results of 96.4% precision, 89.3% recall, and a 92.7% F1-score, surpassing previous statistical approaches to PD detection.
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Bello, A. S., Schneider, J., and Di Pietro, R. (2023). LLD: A Low Latency Detection Solution to Thwart Cryptocurrency Pump & Dumps. In 2023 IEEE International Conference on Blockchain and Cryptocurrency, ICBC 2023. DOI: 10.1109/ICBC56567.2023.10174922.
Han, J., Pei, J., and Tong, H. (2022). Data Mining: Concepts and Techniques. Morgan Kaufmann, Cambridge, MA, 4th edition.
Hu, S., Zhang, Z., Lu, S., He, B., and Li, Z. (2023). Sequence-Based Target Coin Prediction for Cryptocurrency Pump-and-Dump. Proc. ACM Manag. Data, 1(1):6:1–6:19. DOI: 10.1145/3588686.
Jalal, R. N.-U.-D., Alon, I., and Paltrinieri, A. (2021). A bibliometric review of cryptocurrencies as a financial asset. Technology Analysis and Strategic Management. DOI: 10.1080/09537325.2021.1939001.
Kamps, J. and Kleinberg, B. (2018). To the moon: defining and detecting cryptocurrency pump-and-dumps. Crime Science, 7(1). DOI: 10.1186/s40163-018-0093-5.
Kethineni, S. and Cao, Y. (2020). The Rise in Popularity of Cryptocurrency and Associated Criminal Activity. International Criminal Justice Review, 30(3):325–344. DOI: 10.1177/1057567719827051.
Kramer, D. (2005). The Way It Is and the Way It Should Be: Liability Under §10(b) of the Exchange Act and Rule 10b-5 Thereunder for Making False and Misleading Statements as Part of a Scheme to ”Pump and Dump” a Stock. University of Miami Business Law Review, 13(2):243.
La Morgia, M., Mei, A., Sassi, F., and Stefa, J. (2020). Pump and Dumps in the Bitcoin Era: Real Time Detection of Cryptocurrency Market Manipulations. In Proceedings - International Conference on Computer Communications and Networks, ICCCN, volume 2020-August. DOI: 10.1109/ICCCN49398.2020.9209660.
La Morgia, M., Mei, A., Sassi, F., and Stefa, J. (2023). The Doge of Wall Street: Analysis and Detection of Pump and Dump Cryptocurrency Manipulations. ACM Transactions on Internet Technology, 23(1). DOI: 10.1145/3561300.
Moura, M. S., Baroni, L., Ogasawara, E., and Mendonça, D. S. (2024). HD Pump: A Hybrid Detection Approach for Pump-and-Dump Schemes in Cryptocurrency Exchanges. In Simpósio Brasileiro de Banco de Dados (SBBD), pages 757–763. SBC. DOI: 10.5753/sbbd.2024.243293.
Ogasawara, E., Salles, R., Porto, F., and Pacitti, E. (2025). Event Detection in Time Series. Synthesis Lectures on Data Management. Springer Nature Switzerland, Cham, 1 edition. DOI: 10.1007/978-3-031-75941-3.
Rajaei, M. J. and Mahmoud, Q. H. (2023). A Survey on Pump and Dump Detection in the Cryptocurrency Market Using Machine Learning. Future Internet, 15(8). DOI: 10.3390/fi15080267.
Salles, R., Escobar, L., Baroni, L., Zorrilla, R., Ziviani, A., Kreischer, V., Delicato, F., Pires, P. F., Maia, L., Coutinho, R., Assis, L., and Ogasawara, E. (2020). Harbinger: Um framework para integração e análise de métodos de detecção de eventos em séries temporais. In Anais do Simpósio Brasileiro de Banco de Dados (SBBD), pages 73–84. SBC. DOI: 10.5753/sbbd.2020.13626.
Schmitt, W. (2024). Bitcoin trading volumes surge after debut of long-awaited US ETFs. Financial Times.
Shumway, R. H. and Stoffer, D. S. (2017). Time Series Analysis and Its Applications: With R Examples. Springer.
Souza, J., Paixão, E., Fraga, F., Baroni, L., Alves, R. F. S., Belloze, K., Dos Santos, J., Bezerra, E., Porto, F., and Ogasawara, E. (2024). REMD: A Novel Hybrid Anomaly Detection Method Based on EMD and ARIMA. In Proceedings of the International Joint Conference on Neural Networks. DOI: 10.1109/IJCNN60899.2024.10651192.
Steinmetz, F., von Meduna, M., Ante, L., and Fiedler, I. (2021). Ownership, uses and perceptions of cryptocurrency: Results from a population survey. Technological Forecasting and Social Change, 173. DOI: 10.1016/j.techfore.2021.121073.
Takeuchi, J.-I. and Yamanishi, K. (2006). A unifying framework for detecting outliers and change points from time series. IEEE Transactions on Knowledge and Data Engineering, 18(4):482–492. DOI: 10.1109/TKDE.2006.1599387.
Victor, F. and Hagemann, T. (2019). Cryptocurrency pump and dump schemes: Quantification and detection. In IEEE International Conference on Data Mining Workshops, ICDMW, volume 2019-November, pages 244–251. DOI: 10.1109/ICDMW.2019.00045.
Wursthorn, M. (2021). A Bitcoin ETF Is Here. What Does That Mean for Investors? Wall Street Journal.
Xu, J. and Livshits, B. (2019). The anatomy of a cryptocurrency pump-and-dump scheme. In Proceedings of the 28th USENIX Conference on Security Symposium, SEC’19, pages 1609–1625, USA. USENIX Association.
