Development of a method for improving the efficiency of transaction classification in the Bitcoin network using an attention mechanism in graph neural networks

Authors

DOI:

https://doi.org/10.15587/1729-4061.2026.351685

Keywords:

blockchain, BitcoinHeist, GATv2, graph neural networks, transfer learning, active learning

Abstract

The object of the study is the processes of automated transaction classification and Bitcoin address identification for detecting malicious activity in conditions of pseudo-anonymity. The problem is the insufficient effectiveness of algorithms, such as graph convolutional networks, in conditions of strong class imbalance. This discovery is particularly important when less than ten percent of the data is clearly labelled. However, the main difficulty is excessive feature smoothing, which complicates the effective detection of anomalies for dense graphs. The results confirm that the Graph Attention Network v2 (GATv2) model is effective. It achieves an accuracy of 91.19% and an F1 score of 91.11% in testing. In addition, the stability of the approach is confirmed when 15% of topological noise is added to the graph structure. To prove the selectivity of the classifier, the Area Under the Curve (AUC) value of the approach is 0.889. The results are explained by the implementation of a dynamic anisotropic aggregation mechanism that adaptively distributes attention weights. This allows selectively amplifying weak signals of suspicious transactions while ignoring irrelevant connections and noise. A distinctive feature is the model of feature unification through logarithmic normalization of sums and non-linear processing of time intervals. Its uniqueness lies in the use of weighted loss functions and active learning strategies on boundary samples. Two-level transfer learning was applied to the Elliptic and BitcoinHeist datasets. The area of application is integration into real-time anti-money laundering (AML) systems. The approach allows overcoming conceptual shifts when new types of cyber threats emerge. The method detects the activity of CryptoLocker-type extortionists in the absence of data

Author Biographies

Oleksandr Kushnerov, Sumy State University

Doctor of Philosophy (PhD)

Department of Economic Cybernetics

Vladyslav Prosolov, Kharkiv National University of Radio Electronics

PhD Student, Assistant

Department of Information Technology Security

Valerii Dudykevych, Lviv Polytechnic National University

Doctor of Technical Sciences, Professor

Department of Information Security

Serhii Yevseiev, National Technical University “Kharkiv Polytechnic Institute”

Doctor of Technical Sciences, Professor, Head of Department

Department of Cybersecurity

Serhii Povaliaiev, Kharkiv National Automobile and Highway University

PhD, Associate Professor

Department of Machine Components and Theory of Machines and Mechanisms

Yevheniia Ivanchenko, State University of Information and Communication Technologies

Doctor of Technical Sciences, Professor

Director

Educational-Scientific Institute of Cyber Security and Information Protection

Volodymyr Gorbulyk, Yuriy Fedkovych Chernivtsi National University

PhD, Associate Professor

Department of Radioengineering and Information Security

Oleksandr Chechui, Ivan Kozhedub Kharkiv National Air Force University

PhD, Associate Professor

Department of Radioelectronic Systems of Control Points of Air Forces

Dmytro Balagura, Kharkiv National University of Radio Electronics

PhD, Associate Professor

Department of Information Technology Security

Vladyslav Sukhoteplyi, Ivan Kozhedub Kharkiv National Air Force University

Senior Lecturer

Department of Radioelectronic Systems of Control Points of Air Forces

References

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Development of a method for improving the efficiency of transaction classification in the Bitcoin network using an attention mechanism in graph neural networks

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Published

2026-02-27

How to Cite

Kushnerov, O., Prosolov, V., Dudykevych, V., Yevseiev, S., Povaliaiev, S., Ivanchenko, Y., Gorbulyk, V., Chechui, O., Balagura, D., & Sukhoteplyi, V. (2026). Development of a method for improving the efficiency of transaction classification in the Bitcoin network using an attention mechanism in graph neural networks. Eastern-European Journal of Enterprise Technologies, 1(9 (139), 6–18. https://doi.org/10.15587/1729-4061.2026.351685

Issue

Vol. 1 No. 9 (139) (2026): Information and controlling system

Section

Information and controlling system

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Copyright (c) 2026 Oleksandr Kushnerov, Vladyslav Prosolov, Valerii Dudykevych, Serhii Yevseiev, Serhii Povaliaiev, Yevheniia Ivanchenko, Volodymyr Gorbulyk, Oleksandr Chechui, Dmytro Balagura, Vladyslav Sukhoteplyi

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