EGRET: Extortion graph exploration techniques in the bitcoin network

Silivanxay Phetsouvanh; Frederique Oggier; Anwitaman Datta

doi:10.1109/ICDMW.2018.00043

EGRET: Extortion graph exploration techniques in the bitcoin network

Silivanxay Phetsouvanh
, Frederique Oggier
, Anwitaman Datta

Mathematics

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

Abstract

The Bitcoin network is a complex network that records anonymous financial transactions while encapsulating the relationships among its pseudonymous users. This paper proposes graph mining techniques to explore the relationships among wallet addresses (pseudonyms for Bitcoin users) suspected to be involved in a given extortion racket, exploiting the anonymity of the Bitcoin network to collect and launder money. Starting around Bitcoin addresses of potential interest, neighborhood subgraphs are analyzed in terms of path length and confluence to detect suspicious Bitcoin flow and other wallet addresses controlled by the suspected perpetrators. We show with a dataset of the Ashley Madison blackmail campaign from August 2015 how the mechanisms can be used both to estimate the amount of money that was extorted by the suspected perpetrators under the specific blackmail campaign, and also estimate the amount of money handled by them during the same period of time.

Original language	English
Title of host publication	Proceedings - 18th IEEE International Conference on Data Mining Workshops, ICDMW 2018
Editors	Hanghang Tong, Zhenhui Li, Feida Zhu, Jeffrey Yu
Publisher	IEEE Computer Society Press
Pages	244-251
Number of pages	8
ISBN (Electronic)	9781538692882
DOIs	https://doi.org/10.1109/ICDMW.2018.00043
Publication status	Published - 2 Jul 2018
Event	18th IEEE International Conference on Data Mining Workshops - Singapore, Singapore Duration: 17 Nov 2018 → 20 Nov 2018 https://sn.committees.comsoc.org/call-for-papers/the-18th-ieee-international-conference-on-data-mining-icdm-2018/

Publication series

Name	IEEE International Conference on Data Mining Workshops, ICDMW
Volume	2018-November
ISSN (Print)	2375-9232
ISSN (Electronic)	2375-9259

Conference

Conference	18th IEEE International Conference on Data Mining Workshops
Abbreviated title	ICDMW 2018
Country/Territory	Singapore
City	Singapore
Period	17/11/18 → 20/11/18
Internet address	https://sn.committees.comsoc.org/call-for-papers/the-18th-ieee-international-conference-on-data-mining-icdm-2018/

Bibliographical note

Publisher Copyright:
© 2018 IEEE.

Keywords

Ashley Madison Data Breach
Bitcoin
cryptocurrency
extortion
forensics
graph mining algorithms
laundering

ASJC Scopus subject areas

Computer Science Applications
Software

Access to Document

10.1109/ICDMW.2018.00043

Cite this

Phetsouvanh, S., Oggier, F., & Datta, A. (2018). EGRET: Extortion graph exploration techniques in the bitcoin network. In H. Tong, Z. Li, F. Zhu, & J. Yu (Eds.), Proceedings - 18th IEEE International Conference on Data Mining Workshops, ICDMW 2018 (pp. 244-251). Article 8637544 (IEEE International Conference on Data Mining Workshops, ICDMW; Vol. 2018-November). IEEE Computer Society Press. https://doi.org/10.1109/ICDMW.2018.00043

Phetsouvanh, Silivanxay ; Oggier, Frederique ; Datta, Anwitaman. / EGRET : Extortion graph exploration techniques in the bitcoin network. Proceedings - 18th IEEE International Conference on Data Mining Workshops, ICDMW 2018. editor / Hanghang Tong ; Zhenhui Li ; Feida Zhu ; Jeffrey Yu. IEEE Computer Society Press, 2018. pp. 244-251 (IEEE International Conference on Data Mining Workshops, ICDMW).

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title = "EGRET: Extortion graph exploration techniques in the bitcoin network",

abstract = "The Bitcoin network is a complex network that records anonymous financial transactions while encapsulating the relationships among its pseudonymous users. This paper proposes graph mining techniques to explore the relationships among wallet addresses (pseudonyms for Bitcoin users) suspected to be involved in a given extortion racket, exploiting the anonymity of the Bitcoin network to collect and launder money. Starting around Bitcoin addresses of potential interest, neighborhood subgraphs are analyzed in terms of path length and confluence to detect suspicious Bitcoin flow and other wallet addresses controlled by the suspected perpetrators. We show with a dataset of the Ashley Madison blackmail campaign from August 2015 how the mechanisms can be used both to estimate the amount of money that was extorted by the suspected perpetrators under the specific blackmail campaign, and also estimate the amount of money handled by them during the same period of time.",

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author = "Silivanxay Phetsouvanh and Frederique Oggier and Anwitaman Datta",

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Phetsouvanh, S, Oggier, F & Datta, A 2018, EGRET: Extortion graph exploration techniques in the bitcoin network. in H Tong, Z Li, F Zhu & J Yu (eds), Proceedings - 18th IEEE International Conference on Data Mining Workshops, ICDMW 2018., 8637544, IEEE International Conference on Data Mining Workshops, ICDMW, vol. 2018-November, IEEE Computer Society Press, pp. 244-251, 18th IEEE International Conference on Data Mining Workshops, Singapore, Singapore, 17/11/18. https://doi.org/10.1109/ICDMW.2018.00043

EGRET: Extortion graph exploration techniques in the bitcoin network. / Phetsouvanh, Silivanxay; Oggier, Frederique; Datta, Anwitaman.
Proceedings - 18th IEEE International Conference on Data Mining Workshops, ICDMW 2018. ed. / Hanghang Tong; Zhenhui Li; Feida Zhu; Jeffrey Yu. IEEE Computer Society Press, 2018. p. 244-251 8637544 (IEEE International Conference on Data Mining Workshops, ICDMW; Vol. 2018-November).

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

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AB - The Bitcoin network is a complex network that records anonymous financial transactions while encapsulating the relationships among its pseudonymous users. This paper proposes graph mining techniques to explore the relationships among wallet addresses (pseudonyms for Bitcoin users) suspected to be involved in a given extortion racket, exploiting the anonymity of the Bitcoin network to collect and launder money. Starting around Bitcoin addresses of potential interest, neighborhood subgraphs are analyzed in terms of path length and confluence to detect suspicious Bitcoin flow and other wallet addresses controlled by the suspected perpetrators. We show with a dataset of the Ashley Madison blackmail campaign from August 2015 how the mechanisms can be used both to estimate the amount of money that was extorted by the suspected perpetrators under the specific blackmail campaign, and also estimate the amount of money handled by them during the same period of time.

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Phetsouvanh S, Oggier F, Datta A. EGRET: Extortion graph exploration techniques in the bitcoin network. In Tong H, Li Z, Zhu F, Yu J, editors, Proceedings - 18th IEEE International Conference on Data Mining Workshops, ICDMW 2018. IEEE Computer Society Press. 2018. p. 244-251. 8637544. (IEEE International Conference on Data Mining Workshops, ICDMW). doi: 10.1109/ICDMW.2018.00043

EGRET: Extortion graph exploration techniques in the bitcoin network

Abstract

Publication series

Conference

Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

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Cite this