Hybrid Cryptocurrency Pump and Dump Detection
  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.
