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Robust Recurrent Graph Convolutional Network Approach based Sequential Prediction of Illicit Transactions in Cryptocurrencies.

Alarab, I. and Prakoonwit, S., 2024. Robust Recurrent Graph Convolutional Network Approach based Sequential Prediction of Illicit Transactions in Cryptocurrencies. Multimedia Tools and Applications, 83, 58449-58464.

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DOI: 10.1007/s11042-023-17323-4

Abstract

Money laundering has urged the need for machine learning algorithms for combating illicit services in the blockchain of cryptocurrencies due to its increasing complexity. Recent studies have revealed promising results using supervised learning methods in classifying illicit Bitcoin transactions of Elliptic data, one of the largest labelled data of Bitcoin transaction graphs. Nonetheless, all learning algorithms have failed to capture the dark market shutdown event that occurred in this data using its original features. This paper proposes a novel method named recurrent graph neural network model that extracts the temporal and graph topology of Bitcoin data to perform node classification as licit/illicit transactions. The proposed model performs sequential predictions that rely on recent labelled transactions designated by antecedent neighbouring features. Our main finding is that the proposed model against various models on Elliptic data has achieved state-of-the-art with accuracy and f1-score of 98.99% and 91.75%, respectively. Moreover, we visualise a snapshot of a Bitcoin transaction graph of Elliptic data to perform a case study using a backward reasoning process. The latter highlights the effectiveness of the proposed model from the explainability perspective. Sequential prediction leverages the dynamicity of the graph network in Elliptic data.

Item Type:Article
ISSN:1380-7501
Uncontrolled Keywords:Graph Neural Network; Long Short-Term Memory; Bitcoin Blockchain; Anti-Money Laundering
Group:Faculty of Science & Technology
ID Code:39075
Deposited By: Symplectic RT2
Deposited On:02 Nov 2023 13:08
Last Modified:05 Jun 2024 10:54

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