Earticle

초록

영어

The anonymity of Bitcoin transaction has let Bitcoin be used as a medium of illicit activities in the dark-net marketplace that are related to crimes such as selling drugs, weapon, etc. Detecting illicit Bitcoin transaction has been drawing attention from government agencies and academia, since FBI’s investigation from 2011 to 2013, when FBI seized a marketplace, SilkRoad, which trades illicit goods and services only with Bitcoin. However, little research has been conducted to detect illicit Bitcoin transactions. In this paper, we applied data mining approach to detect illicit Bitcoin transaction using a dataset which consists of illicit Bitcoin transaction data released by FBI and legal Bitcoin transaction data. We built several classification models such as RandomForest, Decision Tree(C5.0), and SVM. 10-fold cross-validation reveals that RandomForest outperforms the other two. It is expected that we can reduce the investigation time and cost to detect illicit Bitcoin transactions.

목차

Abstract
 Introduction
 Related Studies
 Background
  Bitcoin
  Silkroad
 Data
  Data Description
  Data Preprocessing
 Method
  Mining Algorithms
 Experiment
  Experiment Setup
  Experiment Results
 Conclusion
 Reference

저자

• Terresa Kim [ Korea University Business School, 145 Anam-Ro, Seoul, Republic of Korea ]
• Yongmu Suh [ Korea University Business School, 145 Anam-Ro, Seoul, Republic of Korea ]

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