Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/88894
Title: EGRET : extortion graph exploration techniques in the Bitcoin network
Authors: Phetsouvanh, Silivanxay
Oggier, Frédérique
Datta, Anwitaman
Keywords: Bitcoin Forensics
Graph Analysis
DRNTU::Engineering::Computer science and engineering
Issue Date: 2018
Source: Phetsouvanh, S., Oggier, F., & Datta, A. (2018). EGRET : extortion graph exploration techniques in the Bitcoin network. Proceedings of 2018 IEEE International Conference on Data Mining Workshops (ICDMW). doi:10.1109/ICDMW.2018.00043
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.
URI: https://hdl.handle.net/10356/88894
http://hdl.handle.net/10220/49145
DOI: https://dx.doi.org/10.1109/ICDMW.2018.00043
Rights: © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: https://doi.org/10.1109/ICDMW.2018.00043
metadata.item.grantfulltext: open
metadata.item.fulltext: With Fulltext
Appears in Collections:SCSE Conference Papers
SPMS Conference Papers

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