Hybrid Cryptocurrency Pump and Dump Detection
This addresses the problem of financial fraud detection for cryptocurrency traders and regulators, but it is incremental as it builds on existing anomaly detection techniques.
The paper tackles the problem of detecting pump and dump schemes in cryptocurrency markets by proposing a hybrid anomaly detection method based on distance and density metrics, which outperforms existing methods in detecting the majority of alleged activities in top-ranked exchange pairs.
Increasingly growing Cryptocurrency markets have become a hive for scammers to run pump and dump schemes which is considered as an anomalous activity in exchange markets. Anomaly detection in time series is challenging since existing methods are not sufficient to detect the anomalies in all contexts. In this paper, we propose a novel hybrid pump and dump detection method based on distance and density metrics. First, we propose a novel automatic thresh-old setting method for distance-based anomaly detection. Second, we propose a novel metric called density score for density-based anomaly detection. Finally, we exploit the combination of density and distance metrics successfully as a hybrid approach. Our experiments show that, the proposed hybrid approach is reliable to detect the majority of alleged P & D activities in top ranked exchange pairs by outperforming both density-based and distance-based methods.