research-article

Improving Streaming Cryptocurrency Transaction Classification via Biased Sampling and Graph Feedback

Authors:

Shaltiel Eloul,

Jacob MendelAuthors Info & Claims

ACSAC '21: Proceedings of the 37th Annual Computer Security Applications Conference

Pages 761 - 772

https://doi.org/10.1145/3485832.3485913

Published: 06 December 2021 Publication History

Abstract

We show that knowledge of wallet addresses from the current time state of a blockchain network, such as Bitcoin, increases the performance of illicit activity detection. Based on this finding we introduce two new methods for the sampling of classifier training data so that precedence is given to transaction information from the recent past and the current time state. This sampling enables streaming classification in which a decision on the class of a transaction needs to be made based on data seen to date. Our new approach provides insight into how the dynamics of the blockchain network plays a central role in the detection of illicit transactions, and is independent of the classifier choice. Our proposed sampling methods enable graph convolution network (GCN) and random forest (RF) classifiers to better adapt to changes in the network due to significant events, such as the closure of a large ‘Darknet’ marketplace. We introduce Graphlet spectral correlation analysis for exposing the effect of such network re-organisation due to major events. Finally, based on our analysis, we propose a new two-stage random forest classifier that feeds back intermediate predictions of neighbours to improve the classification decision. Our methodology enables practical streaming classification, even in the scenario of very limited information on the feature space of each transaction.

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Cited By

Alarab IPrakoonwit S(2023)Robust recurrent graph convolutional network approach based sequential prediction of illicit transactions in cryptocurrenciesMultimedia Tools and Applications10.1007/s11042-023-17323-483:20(58449-58464)Online publication date: 22-Dec-2023
https://doi.org/10.1007/s11042-023-17323-4
Zhi XSatsangi YMoran SEloul SAl Hasan MXiong L(2022)Ledgit: A Service to Diagnose Illicit Addresses on Blockchain using Multi-modal Unsupervised LearningProceedings of the 31st ACM International Conference on Information & Knowledge Management10.1145/3511808.3557212(5069-5073)Online publication date: 17-Oct-2022
https://dl.acm.org/doi/10.1145/3511808.3557212

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Improving Streaming Cryptocurrency Transaction Classification via Biased Sampling and Graph Feedback

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ACSAC '21: Proceedings of the 37th Annual Computer Security Applications Conference

December 2021

1077 pages

ISBN:9781450385794

DOI:10.1145/3485832

Copyright © 2021 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Published: 06 December 2021

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ACSAC '21

ACSAC '21: Annual Computer Security Applications Conference

December 6 - 10, 2021

Virtual Event, USA

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Cited By

Alarab IPrakoonwit S(2023)Robust recurrent graph convolutional network approach based sequential prediction of illicit transactions in cryptocurrenciesMultimedia Tools and Applications10.1007/s11042-023-17323-483:20(58449-58464)Online publication date: 22-Dec-2023
https://doi.org/10.1007/s11042-023-17323-4
Zhi XSatsangi YMoran SEloul SAl Hasan MXiong L(2022)Ledgit: A Service to Diagnose Illicit Addresses on Blockchain using Multi-modal Unsupervised LearningProceedings of the 31st ACM International Conference on Information & Knowledge Management10.1145/3511808.3557212(5069-5073)Online publication date: 17-Oct-2022
https://dl.acm.org/doi/10.1145/3511808.3557212

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