Please use this identifier to cite or link to this item: http://hdl.handle.net/10125/70801

Follow the money: Revealing risky nodes in a Ransomware-Bitcoin network

File Size Format  
0154.pdf 1.07 MB Adobe PDF View/Open

Item Summary

Title:Follow the money: Revealing risky nodes in a Ransomware-Bitcoin network
Authors:Turner, Adam
Mccombie, Stephen
Uhlmann, Allon
Keywords:Machine Learning and Predictive Analytics in Accounting, Finance, and Management
bitcoin
cryptocurrency
graph analytics
machine learning
show 2 moreransomware
risk
show less
Date Issued:05 Jan 2021
Abstract:This paper demonstrates the use of network analysis to identify core nodes associated with ransomware attacks in cryptocurrency transaction networks. The method helps trace the cyber entities involved in cryptocurrency attacks and supports intelligence efforts to identify and disrupt cryptocurrency networks. A data corpus is built by the unsupervised machine learning graph algorithm ‘DeepWalk’ [1]. DeepWalk evaluates the position of nodes within networks. It compares the relative position of different nodes (similarity) and identifies those whose removal would most affect the network (riskiness). This method helps identify on the blockchain the key nodes that are involved in the execution of a ransomware attack. When applied to the ransomware “cash out” graph, the method derived “riskiness” scores for specific nodes. Analysing the derived “riskiness” at a community level (groups of nodes in the network) provides an enhanced granularity for identifying and targeting influential nodes. Such insight could potentially support both intelligence and forensics investigations.
Pages/Duration:13 pages
URI:http://hdl.handle.net/10125/70801
ISBN:978-0-9981331-4-0
DOI:10.24251/HICSS.2021.189
Rights:Attribution-NonCommercial-NoDerivatives 4.0 International
https://creativecommons.org/licenses/by-nc-nd/4.0/
Appears in Collections: Machine Learning and Predictive Analytics in Accounting, Finance, and Management


Please email libraryada-l@lists.hawaii.edu if you need this content in ADA-compliant format.

This item is licensed under a Creative Commons License Creative Commons