A Model for Detecting Cryptocurrency Transactions with Discernible Purpose

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

doi:10.1109/ICUFN.2019.8806126

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 proceeding › Conference 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 language	English
Title of host publication	ICUFN 2019 - 11th International Conference on Ubiquitous and Future Networks
Publisher	IEEE Computer Society
Pages	713-717
Number of pages	5
ISBN (Electronic)	9781728113395
DOIs	https://doi.org/10.1109/ICUFN.2019.8806126
Publication status	Published - 2019 Jul
Event	11th International Conference on Ubiquitous and Future Networks, ICUFN 2019 - Zagreb, Croatia Duration: 2019 Jul 2 → 2019 Jul 5

Publication series

Name	International Conference on Ubiquitous and Future Networks, ICUFN
Volume	2019-July
ISSN (Print)	2165-8528
ISSN (Electronic)	2165-8536

Conference

Conference	11th International Conference on Ubiquitous and Future Networks, ICUFN 2019
Country/Territory	Croatia
City	Zagreb
Period	19/7/2 → 19/7/5

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

Access to Document

10.1109/ICUFN.2019.8806126

Cite this

Baek, H., Oh, J., Kim, C. Y., & Lee, K. (2019). A Model for Detecting Cryptocurrency Transactions with Discernible Purpose. In ICUFN 2019 - 11th International Conference on Ubiquitous and Future Networks (pp. 713-717). Article 8806126 (International Conference on Ubiquitous and Future Networks, ICUFN; Vol. 2019-July). IEEE Computer Society. https://doi.org/10.1109/ICUFN.2019.8806126

A Model for Detecting Cryptocurrency Transactions with Discernible Purpose. / Baek, Hyochang; Oh, Junhyoung; Kim, Chang Yeon et al.
ICUFN 2019 - 11th International Conference on Ubiquitous and Future Networks. IEEE Computer Society, 2019. p. 713-717 8806126 (International Conference on Ubiquitous and Future Networks, ICUFN; Vol. 2019-July).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Baek, H, Oh, J, Kim, CY & Lee, K 2019, A Model for Detecting Cryptocurrency Transactions with Discernible Purpose. in ICUFN 2019 - 11th International Conference on Ubiquitous and Future Networks., 8806126, International Conference on Ubiquitous and Future Networks, ICUFN, vol. 2019-July, IEEE Computer Society, pp. 713-717, 11th International Conference on Ubiquitous and Future Networks, ICUFN 2019, Zagreb, Croatia, 19/7/2. https://doi.org/10.1109/ICUFN.2019.8806126

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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.",

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A Model for Detecting Cryptocurrency Transactions with Discernible Purpose

Abstract

Publication series

Conference

Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

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