HD Pump: A Hybrid Detection Approach for Pump-and-Dump Schemes in Cryptocurrency Exchanges
Resumo
The adoption of cryptocurrencies has created a favorable environment for price manipulation practices, such as pump-and-dump (PD) schemes. These schemes aim to artificially inflate an asset's price, followed by a rapid sell-off, which may harm unaware investors. Given the brief duration of PD scheme effects, their impact on the asset's price series can be considered anomalies. Most studies rely on classification-based anomaly detection techniques to identify the PD event, which presents an opportunity to explore techniques beyond anomaly detection. To address this, we explore the combination of anomaly and change point detection to enhance pump-and-dump scheme detection. We introduce HD Pump, a hybrid detection method that integrates both techniques. Experimental results demonstrate that our hybrid approach significantly improves performance, achieving a 6.7% increase in precision and a 9.3% increase in recall compared to the benchmark method that solely uses anomaly detection.
Referências
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.
Kamps, J. and Kleinberg, B. (2018). To the moon: defining and detecting cryptocurrency pump-and-dumps. Crime Science, 7(1).
Kethineni, S. and Cao, Y. (2020). The Rise in Popularity of Cryptocurrency and Associated Criminal Activity. International Criminal Justice Review, 30(3):325 – 344.
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.
Ogasawara, E., Salles, R., Lima, J., Baroni, L., Castro, A., Carvalho, L., Borges, H., Carvalho, D., Coutinho, R., Bezerra, E., Pacitti, E., and Porto, F. (2023). harbinger: A Unified Time Series Event Detection Framework.
Olteanu, M., Rossi, F., and Yger, F. (2023). Meta-survey on outlier and anomaly detection. Neurocomputing, 555.
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).
Schmitt, W. (2024). Bitcoin trading volumes surge after debut of long-awaited US ETFs. Technical report, [link].
Shumway, R. H. and Stoffer, D. S. (2017). Time Series Analysis and Its Applications: With R Examples. Springer.
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, 173:121073.
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.
Truong, C., Oudre, L., and Vayatis, N. (2020). Selective review of offline change point detection methods. Signal Processing, 167.
Victor, F. and Hagemann, T. (2019). Cryptocurrency Pump and Dump Schemes: Quantification and Detection. In 2019 International Conference on Data Mining Workshops (ICDMW), pages 244–251.
Wursthorn, M. (2021). A Bitcoin ETF Is Here. What Does That Mean for Investors? Technical report, [link].
Xu, J. and Livshits, B. (2019). The anatomy of a cryptocurrency pump-and-dump scheme. In Proceedings of the 28th USENIX Security Symposium, pages 1609 – 1625.