论文标题
混合加密货币泵和垃圾场检测
Hybrid Cryptocurrency Pump and Dump Detection
论文作者
论文摘要
越来越多的加密货币市场已成为骗子运行泵和倾倒计划的蜂巢，这被认为是交易市场中的一种异常活动。时间序列中的异常检测具有挑战性，因为现有方法不足以在所有情况下检测异常。在本文中，我们提出了一种基于距离和密度指标的新型混合泵和转储检测方法。首先，我们为基于距离的异常检测提出了一种新型的自动阈值旧设置方法。其次，我们提出了一个新型的度量，称为密度基于密度的异常检测的密度评分。最后，我们成功利用了密度和距离指标的组合作为混合方法。我们的实验表明，提出的混合方法可靠，可以通过优于基于密度的方法和基于距离的方法来检测最高排名交换对中所谓的P＆D活动。
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.
