【Author】
Monamo, Patrick; Marivate, Vukosi; Twala, Bheki
【Source】2016 INFORMATION SECURITY FOR SOUTH AFRICA - PROCEEDINGS OF THE 2016 ISSA CONFERENCE
【Abstract】The rampant absorption of Bitcoin as a cryptographic currency, along with rising cybercrime activities, warrants utilization of anomaly detection to identify potential fraud. Anomaly detection plays a pivotal role in data mining since most outlying points contain crucial information for further investigation. In the financial world which the Bitcoin network is part of by default, anomaly detection amounts to fraud detection. This paper investigates the use of trimmed k-means, that is capable of simultaneous clustering of objects and fraud detection in a multivariate setup, to detect fraudulent activity in Bitcoin transactions. The proposed approach detects more fraudulent transactions than similar studies or reports on the same dataset.
【Keywords】cybercrime; anomaly; outlier; trimmed k-means; data mining
【摘要】比特币作为一种加密货币的泛滥，以及日益增多的网络犯罪活动，需要使用异常检测来识别潜在的欺诈行为。异常检测在数据挖掘中起着至关重要的作用，因为大多数离群点都包含着进一步研究的重要信息。在默认情况下，比特币网络是金融世界的一部分，异常检测等同于欺诈检测。本文研究了裁剪k均值的使用，它能够在多元设置中同时对对象聚类和欺诈检测，以检测比特币交易中的欺诈活动。与相同数据集上的类似研究或报告相比，所提出的方法检测出更多的欺诈交易。
【关键词】网络犯罪;异常;离群值;k - means聚类;数据挖掘
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