A Model for Detecting Cryptocurrency Transactions with Discernible Purpose

Hyochang Baek, Junhyoung Oh, Chang Yeon Kim, Kyungho Lee

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    41 Citations (Scopus)

    Abstract

    The perpetration of financial fraud progresses parallel with the innovation in the field of finance. Consequently, the emergence of the blockchain technology has also manifested financial transaction obfuscation through the use of de-anonymization of the blockchain technology. This study identifies the suspicious transaction from Binance, an open-source cryptocurrency, through the means of defining and detecting the cryptocurrency wallets. By drawing the metadata of 38,526 wallets from etherscan.io, this study investigates the transactions with discernible purpose. This study performed an unsupervised learning expectation maximization (EM) algorithm to cluster the data set. Based on the features engineered from the unsupervised learning, we performed an anomaly detection using Random Forest (RF). In this study, we offered an insight into labeling the cryptocurrency wallets by providing a model for detecting the cryptocurrency with anomalous transactions. We advocate that labeling the wallets with discernible transactions may help financial institutions, private sectors, financial intelligence, and government agencies identify and detect the transactions with illicit activities.

    Original languageEnglish
    Title of host publicationICUFN 2019 - 11th International Conference on Ubiquitous and Future Networks
    PublisherIEEE Computer Society
    Pages713-717
    Number of pages5
    ISBN (Electronic)9781728113395
    DOIs
    Publication statusPublished - 2019 Jul
    Event11th International Conference on Ubiquitous and Future Networks, ICUFN 2019 - Zagreb, Croatia
    Duration: 2019 Jul 22019 Jul 5

    Publication series

    NameInternational Conference on Ubiquitous and Future Networks, ICUFN
    Volume2019-July
    ISSN (Print)2165-8528
    ISSN (Electronic)2165-8536

    Conference

    Conference11th International Conference on Ubiquitous and Future Networks, ICUFN 2019
    Country/TerritoryCroatia
    CityZagreb
    Period19/7/219/7/5

    Bibliographical note

    Funding Information:
    ACKNOWLEDGEMENT This research was supported by the Institute for Information c& ommunications Technology Planning &Evaluation(IITP) grant funded by the Korea government(MSIT) (No.2017-0-01853, Machine Learning based Intelligent Malware Analysis Platform)

    Publisher Copyright:
    © 2019 IEEE.

    Keywords

    • Anti-Money Laundering
    • Blockchain
    • Cryptocurrency
    • Ethereum
    • Machine Learning
    • Smart Contract

    ASJC Scopus subject areas

    • Computer Networks and Communications
    • Computer Science Applications
    • Hardware and Architecture

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